1. Bibliographic Information

1.1. Title

The central topic of the paper is "GaussGym: An open-source real-to-sim framework for learning locomotion from pixels." It focuses on creating a high-throughput, photorealistic simulation environment for training robot locomotion and navigation policies using visual inputs.

1.2. Authors

The authors are:

• Alejandro Escontrela (UC Berkeley)

• Justin Kerr (UC Berkeley)

• Arthur Allshire (UC Berkeley)

• Jonas Frey (ETH Zurich)

• Rocky Duan (Amazon FAR)

• Carmelo Sferrazza (UC Berkeley, Amazon FAR)

• Pieter Abbeel (UC Berkeley, Amazon FAR)

  Carmelo Sferrazza and Pieter Abbeel are noted for work done while at UC Berkeley, and Rocky Duan, Carmelo Sferrazza, and Pieter Abbeel are affiliated with Amazon FAR (Frontier AI & Robotics). Their research backgrounds appear to be in robotics, reinforcement learning, computer vision, and simulation, as indicated by their affiliations and the paper's content.

1.3. Journal/Conference

The paper is published at (UTC) 2025-10-17T06:34:52.000Z, which suggests it is a recent or forthcoming publication. The provided link is an arXiv preprint (https://arxiv.org/abs/2510.15352), indicating it has not yet undergone formal peer review for a specific conference or journal at the time of this analysis, but is made publicly available for early dissemination and feedback. arXiv is a reputable platform for preprints in fields like AI, robotics, and physics.

1.4. Publication Year

2025

1.5. Abstract

GaussGym introduces a novel framework for photorealistic robot simulation by integrating 3D Gaussian Splatting as a renderer within vectorized physics simulators like IsaacGym. This integration achieves exceptionally high speed, exceeding 100,000 steps per second on consumer GPUs, while maintaining high visual fidelity across various tasks. The framework also demonstrates applicability in sim-to-real robotics. Beyond depth-based sensing, the paper highlights how rich visual semantics from RGB inputs enhance navigation and decision-making, such as avoiding undesirable regions. GaussGym simplifies the creation of realistic training worlds by incorporating thousands of environments from diverse sources, including iPhone scans, large-scale scene datasets (e.g., GrandTour, ARKit), and outputs from generative video models (e.g., Veo). This work effectively bridges high-throughput simulation and high-fidelity perception, thereby advancing scalable and generalizable robot learning. All code and data will be open-sourced.

1.6. Original Source Link

https://arxiv.org/abs/2510.15352 (Preprint) PDF Link: https://arxiv.org/pdf/2510.15352v1.pdf

2. Executive Summary

2.1. Background & Motivation

The core problem GaussGym aims to solve is the limitation of existing robot simulation frameworks in fully leveraging visual information for reinforcement learning (RL) of robot locomotion and navigation. While sim-to-real methods, where policies are trained in simulation and transferred to real robots, have advanced significantly for physical fidelity, they struggle with visual realism and throughput.

• Why this problem is important: For mobile robots to operate effectively in complex, unstructured real-world environments, they need to perceive their surroundings accurately using visual cues. Many crucial affordances and obstacles (e.g., crosswalks, puddles, specific colored features) are primarily detectable through visual observations, not just geometry (like depth or LiDAR). Current simulators, even GPU-accelerated ones, often provide visual information that is either too slow, too inaccurate, or lacks the semantic richness of real-world RGB images. This forces most perceptive locomotion frameworks to rely on LiDAR or depth inputs, which restricts policies from exploiting semantic cues and limits the complexity of tasks that can be realistically pursued in simulation.

• The paper's entry point or innovative idea: GaussGym proposes to bridge this visual sim-to-real gap by integrating recent advances in 3D reconstruction and differentiable rendering, specifically 3D Gaussian Splatting (3DGS), into high-throughput, vectorized physics simulators. This allows for photorealistic rendering of diverse real-world and generative model-generated environments at unprecedented speeds, directly enabling RL policies to learn from RGB pixels.

2.2. Main Contributions / Findings

The paper makes the following primary contributions:

1. GaussGym Framework: It introduces an open-source, fast, and photorealistic simulator named GaussGym. This framework includes 2,500 scenes and supports diverse scene creation from manual scans (e.g., iPhone), open-source datasets (e.g., GrandTour, ARKit), and outputs from generative video models (e.g., Veo). This significantly expands the diversity and realism of training environments available for robot learning.

2. Addressing the Visual Sim-to-Real Gap: The paper shares findings on improving visual sim-to-real transfer. It demonstrates that incorporating geometry reconstruction as an auxiliary task during training significantly enhances stair-climbing performance of vision-based policies.

3. Semantic Reasoning from RGB: It showcases that RGB navigation policies can perform semantic reasoning in a goal-reaching task. These policies, trained on pixels, successfully avoid undesired regions that are undetectable by depth-only policies. This highlights the crucial advantage of RGB input for richer environmental understanding.

   In summary, GaussGym provides a platform for scalable and generalizable robot learning by combining high-throughput simulation with high-fidelity perception, addressing a key limitation in current sim-to-real RL paradigms.

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

To understand GaussGym, a reader should be familiar with several core concepts in robotics, machine learning, and computer graphics:

• Reinforcement Learning (RL): Reinforcement Learning is a paradigm in machine learning where an agent learns to make decisions by performing actions in an environment to maximize a cumulative reward signal. The agent observes the state of the environment, takes an action, and receives a reward and a new state. The goal is to learn a policy—a mapping from states to actions—that yields the highest expected reward over time.

  • Sim-to-Real: This is a common approach in RL for robotics where policies are first trained in a simulated environment and then deployed on a real physical robot. The goal is to leverage the speed and safety of simulation while achieving good performance in the real world. A key challenge is the sim-to-real gap, which refers to the discrepancies between the simulated and real environments that can cause policies trained in simulation to perform poorly in reality. GaussGym specifically addresses the visual sim-to-real gap.
  • Asymmetric Actor-Critic: A common RL architecture where the actor (which determines the policy/actions) and critic (which estimates the value of states/actions) use different inputs or network structures. Often, the critic has access to "privileged information" (like ground-truth physics states) during training, which is not available to the actor during deployment. In GaussGym, the policy (actor) learns directly from visual input, but the training might leverage ground truth geometry for auxiliary tasks.

• Physics Simulators: These are software environments that mimic the physical behavior of objects and robots. They are essential for RL in robotics because they allow for safe, rapid, and parallelized experimentation.

  • CPU-based Simulators (e.g., MuJoCo, PyBullet, RaiSim): Earlier generations of physics simulators that run computations primarily on the CPU. While effective for physics, they can be a bottleneck for RL training due to their sequential nature and limited parallelization.
  • GPU-accelerated Simulators (e.g., Isaac Gym, Isaac Sim, Genesis): Newer simulators designed to leverage the parallel processing power of GPUs. This allows for running thousands of simulated environments simultaneously, drastically speeding up RL training. Isaac Gym is a prominent example, and GaussGym integrates with it.
  • Vectorized Physics Simulators: These are GPU-accelerated simulators that perform physics calculations for many independent environments in parallel, often on a single GPU. This massively increases throughput (steps per second).

• 3D Scene Representation & Rendering: How virtual environments are represented and drawn.

  • RGB Pixels: The standard color image format, consisting of red, green, and blue color channels. GaussGym aims to train robots directly from these rich visual inputs.
  • Depth Maps/LiDAR: Depth maps provide information about the distance of surfaces from the camera, while LiDAR (Light Detection and Ranging) sensors measure distances by emitting pulsed laser light. These provide geometric information but lack semantic cues (e.g., color, texture, labels) that RGB images offer.
  • Neural Radiance Fields (NeRFs): NeRFs (Mildenhall et al., 2020) are a technique for synthesizing novel views of a complex 3D scene from a set of 2D images. They represent a scene as a continuous volumetric function, typically modeled by a Multi-Layer Perceptron (MLP), that predicts color and density at any point in space. NeRFs achieve high visual quality but are computationally intensive for training and rendering (slow raytracing).
  • 3D Gaussian Splatting (3DGS): 3DGS (Kerbl et al., 2023) is a newer radiance field representation that models a scene as a collection of 3D Gaussians, each with properties like position, covariance (shape), opacity, and spherical harmonics (color/light interaction). Crucially, 3DGS can be differentiably rasterized extremely quickly on modern GPU hardware, offering similar photorealism to NeRFs but with much higher rendering throughput. GaussGym leverages this key technology.
  • Photorealism: The degree to which an image or simulation appears realistic, resembling a photograph.
  • Semantic Cues: Information embedded in visual data that relates to the meaning or identity of objects and regions in a scene (e.g., "this is a puddle," "this is a crosswalk").

• 3D Reconstruction: The process of capturing the shape and appearance of real-world objects or scenes into a 3D digital model.

  • Structure from Motion (SfM): A photogrammetric range imaging technique for estimating 3D structures from 2D image sequences.
  • Visual-Geometric Alignment: Ensuring that the visual representation (e.g., 3DGS) and the geometric representation (e.g., collision mesh) of a scene are accurately aligned in the same coordinate system.

3.2. Previous Works

The paper contextualizes GaussGym by discussing related work across sim-to-real RL for locomotion, scene generation, and the application of radiance fields in robotics.

• Sim-to-Real RL for Locomotion:

  • Early Simulators: MuJoCo (Todorov et al., 2012), PyBullet (Coumans & Bai, 2016-2021), and RaiSim (Hwangbo et al., 2018) were foundational in enabling RL locomotion policies to transfer from simulation to real robots (Tan et al., 2018). These were typically CPU-based.
  • GPU-accelerated Simulators: The advent of GPU-accelerated simulators like Isaac Gym (Makoviychuk et al., 2021a), Isaac Sim (Makoviychuk et al., 2021b), ManiSkill (Tao et al., 2024), and Genesis (Genesis, 2024) democratized RL training by allowing massive parallelization on consumer hardware. These platforms have driven advances in legged locomotion (Rudin et al., 2021) and navigation (Lee et al., 2024).
  • Limitations: Despite advances, most deployed locomotion policies still rely on geometric inputs (depth, elevation maps) or proprioceptive inputs (robot's internal state). This is due to the visual sim-to-real gap, lack of diverse photorealistic assets, and the high throughput required for RL.

• Scene Generation:

  • Heuristic/Procedural Generation: Methods like those used in (Rudin et al., 2021) and (Lee et al., 2024) create environments based on rules, effective for geometry but lacking meaningful visual appearance.
  • Textured Asset Composition: Using textured meshes from asset libraries (e.g., ReplicaCAD (Szot et al., 2021), LeVerb (Xue et al., 2025), AI2-THOR (Kolve et al., 2017)) or specialized 3D scanners (Chang et al., 2017; Xia et al., 2018) integrated into frameworks like Habitat (Ramakrishnan et al., 2021). These often result in lower visual fidelity compared to real-world captures.
  • NeRF2Real (Byravan et al., 2023): This approach uses NeRF to capture scenes for improved visual fidelity, followed by mesh extraction and manual post-processing to train locomotion policies. However, it's computationally expensive due to slow raytracing and lacks vectorization support.
  • LucidSim (Yu et al., 2024): A related work that also uses a splat-integrated simulator for evaluating locomotion policies. It employs ControlNet for generating visual data from depth maps and 3DGS for real-to-sim, requiring manual alignment and limited to smartphone scans.
  • Generative Models: The paper notes the potential of world and video models (DeepMind, 2025; Bruce et al., 2024; Google DeepMind, 2025; Wan et al., 2025) for generating photorealistic, multi-view consistent video, suggesting their use for scalable 3D asset creation, despite their slow inference speed.

• Radiance Fields in Robotics:

  • NeRF applications: Early work leveraged NeRFs for high-quality visual reconstruction in grasping (Kerr et al., 2022; Ichnowski et al., 2020) and language-guided manipulation (Rashid et al., 2023; Shen et al., 2023). NeRFs have also been used as differentiable collision representations for navigation (Adamkiewicz et al., 2022) and visual simulators for drone flight or autonomous driving (Khan et al., 2024; Chen et al., 2025).
  • 3DGS applications: 3DGS mitigates NeRF's slow training speed. It has been used for language-guided robot grasping, persistent Gaussian representations for manipulation, and visual imitation (Zheng et al., 2024; Qin et al., 2023; Qiu et al., 2024; Yu et al., 2025a;b; Kerr et al., 2024).

3.3. Technological Evolution

The field has evolved from relying on rigid-body physics in CPU-based simulators with basic visual rendering, to GPU-accelerated simulators that enable massively parallel RL but still often lack realistic visual fidelity. Concurrently, 3D reconstruction has progressed from textured meshes to Neural Radiance Fields (NeRFs), offering high photorealism but slow rendering. The recent emergence of 3D Gaussian Splatting (3DGS) provides NeRF-level photorealism with significantly faster, differentiable rendering.

GaussGym positions itself at the intersection of these advancements. It leverages GPU-acceleraccelerated physics (like IsaacGym) and combines it with 3DGS for visual rendering. This bridges the throughput of vectorized physics with the photorealism and differentiability of modern radiance fields, overcoming the limitations of previous attempts like NeRF2Real (too slow) and LucidSim (lacked vectorization, required manual alignment).

3.4. Differentiation Analysis

The core differences and innovations of GaussGym compared to related work, particularly LucidSim, LeVerb, and IsaacLab, are highlighted in Table 1 from the paper.

The following are the results from Table 1 of the original paper:

†: Vectorized across 4096 envs on RTX4090. ‡: Vectorized across 768 envs on RTX4090.

Here's a breakdown of GaussGym's differentiation:

• Photorealism: GaussGym achieves photorealism (✓) similar to LucidSim (✓), unlike LeVerb and IsaacLab (X) which rely on traditional raytracing of textured meshes or randomization over primitives, leading to less realistic visuals.

• Temporally Consistent Rendering: GaussGym explicitly ensures temporal consistency (✓), which is crucial for dynamic robot interactions and motion blur, a feature missing in LucidSim (X) which uses ControlNet for image generation, potentially leading to frame-to-frame inconsistencies. LeVerb and IsaacLab (✓) also maintain temporal consistency through their rendering pipelines.

• Throughput (Vectorized & Per Environment): This is where GaussGym significantly surpasses others.

  • It achieves an unprecedented 100,000 steps per second vectorized across 4096 environments on an RTX 4090. This translates to 25 FPS per environment.
  • In contrast, LucidSim is limited to single environment rendering at 3 FPS. IsaacLab achieves 800 FPS vectorized over 768 environments (approx. 1 FPS per env), which is significantly lower than GaussGym's per-environment rate when scaled.
  • The extremely high throughput allows for massively parallel reinforcement learning in diverse, photorealistic environments.

• Renderer: GaussGym utilizes 3D Gaussian Splatting, a cutting-edge radiance field technique known for its balance of visual fidelity and speed. LucidSim uses ControlNet for generating visuals, LeVerb and IsaacLab use traditional raytracing. 3DGS is key to GaussGym's speed and photorealism.

• Scene Creation: GaussGym boasts highly flexible scene creation, accepting smartphone scans, pre-existing datasets, and importantly, video model outputs (e.g., Veo). This makes it easy to generate thousands of diverse, complex environments. LucidSim uses hand-designed scenes and relies on Polycam scans with manual alignment. LeVerb also uses hand-designed scenes, and IsaacLab primarily uses randomization over primitives, which lacks real-world complexity.

• Scalability and Integration: While LucidSim also incorporates 3DGS, GaussGym explicitly emphasizes its tight integration with massively parallel physics simulation and a framework designed to scale to thousands of scanned scenes with automatic alignment, which LucidSim lacks.

  In essence, GaussGym distinguishes itself by combining the photorealistic rendering capabilities of 3DGS with the high-throughput of vectorized physics simulators, all within a flexible framework that simplifies the ingestion and automatic processing of diverse real-world and generative model data for large-scale robot learning.

4. Methodology

4.1. Principles

The core principle behind GaussGym is to integrate 3D Gaussian Splatting (3DGS) as a high-fidelity, high-throughput photorealistic renderer directly into existing vectorized physics simulators like IsaacGym. This allows robot learning algorithms to train visuomotor policies from RGB pixels in diverse, realistic environments at speeds previously unattainable with photorealistic rendering. The intuition is that by coupling advanced 3D reconstruction techniques with GPU-accelerated physics, robots can learn to perceive and interact with complex visual semantics in real-world scenarios, thereby narrowing the visual sim-to-real gap.

4.2. Core Methodology In-depth (Layer by Layer)

The GaussGym pipeline involves several key stages, from data ingestion to rendering and policy training:

4.2.1. Overall Architecture (Figure 2 and Figure 4)

The overall GaussGym pipeline starts with diverse data sources and processes them to generate both 3D Gaussian Splats for rendering and collision meshes for physics simulation. These assets are then integrated into a vectorized physics engine where robots are simulated, and photorealistic RGB and depth images are rendered in parallel.

As shown in Figure 2 and further illustrated by Figure 4, the system ingests data from various sources:

• Posed Datasets: Pre-calibrated datasets where camera intrinsics (e.g., focal length, principal point) and extrinsics (e.g., camera position and orientation) are already known, such as ARKitScenes (Baruch et al., 2021) and GrandTour (Frey et al., 2025).

• Casual Smartphone Scans: Data captured using a smartphone, which may or may not have intrinsic calibration information.

• Unposed RGB Sequences from Video Generation Models: Outputs from generative video models like Veo (Google DeepMind, 2025), which produce RGB video frames but lack camera pose information.

  该图像是示意图，展示了GaussGym如何从各种数据源（如RGB、内外参、点云等）中获取信息，并通过VGGT处理，最终实现高效的图形渲染和物理模拟。图中说明了数据流的各个环节，强调了高效性与视觉表现的结合。

Figure 2: Data collection overview: GaussGym ingests data from various data sources and processes them with VGGT (Wang et al., 2025) to obtain extrinsics, intrinsics, and point clouds with normals. The former two data products are used to train 3D Gaussian Splats for rendering, while the latter two are used to estimate the scene collision mesh.

该图像是示意图，展示了GaussGym在不同提示下生成的虚拟环境。左侧显示了一个幻想世界场景，而右侧则是类似《银翼杀手》的街道环境。通过Veo模型，GaussGym能够将这些提示转化为可供机器人学习的 高清晰度训练世界。

Figure 4: GaussGym ingests a variety of datasets - including video model outputs - to produce photorealistic training environments for robot learning.

4.2.2. Data Standardization and Intermediate Representation

All ingested data is standardized into a common gravity-aligned reference frame. The crucial component for this step is the Visually Grounded Geometry Transformer (VGGT) (Wang et al., 2025).

• VGGT Processing: VGGT is a model designed to process diverse visual inputs (including unposed video) and robustly estimate fundamental 3D reconstruction elements:
  • Camera intrinsics: Parameters describing the camera's optical properties (e.g., focal length, sensor size).
  • Camera extrinsics: Parameters describing the camera's position and orientation in the world.
  • Dense point clouds: A set of data points in a 3D coordinate system, representing the external surface of an object or environment.
  • Surface normals: Vectors indicating the outward direction perpendicular to a surface at a given point.

4.2.3. Scene Reconstruction: Meshes and Gaussian Splats

From the intermediate representations produced by VGGT, two main types of assets are generated, which are automatically aligned in a shared global frame:

1. Collision Mesh Generation:

   • The dense point clouds and surface normals obtained from VGGT are used as input to a Neural Kernel Surface Reconstruction (NKSR) (Huang et al., 2023) module.
   • NKSR is employed to produce high-quality meshes. These meshes are primarily used for physics simulation, specifically for collision detection and contact handling within the vectorized physics engine.

2. 3D Gaussian Splat Generation for Rendering:

   • 3D Gaussian Splats are initialized directly from the VGGT point clouds.
   • This point-cloud initialization is critical as it greatly improves geometric fidelity and accelerates convergence during the 3DGS training process. The Gaussian splats will then be optimized to represent the photorealistic appearance of the scene.
   • This approach ensures precise visual-geometric alignment, extending upon prior work like LucidSim which often required manual registration between meshes and 3DGS.

4.2.4. 3D Gaussian Splatting as a Drop-in Renderer

Once the Gaussian splats for a scene are reconstructed and optimized, they are integrated into the vectorized physics simulator (e.g., IsaacGym) as a drop-in renderer.

• Parallel Rasterization: Unlike traditional raytracing or rasterization pipelines that process scenes sequentially or struggle with vectorization, 3DGS is highly amenable to parallel execution. Gaussian splats are rasterized across multiple simulated environments simultaneously.

• Efficient GPU Utilization: GaussGym utilizes multi-threaded PyTorch kernels to batch-render splats across thousands of environments. This ensures efficient GPU utilization and supports distributed training.

• Photorealistic Output: This process produces photorealistic RGB images and, importantly, depth maps as a direct by-product of the Gaussian Splatting rasterization process without additional rendering time. An example of simultaneous RGB and depth rendering is shown in Figure 5.

  该图像是图示，显示了GaussGym在RGB和深度渲染中的应用。图中包含两个机器人在不同场景中的渲染效果，左下角展示了RGB和深度图。此方法利用高效的Gaussian Splatting过程，在不增加渲染时间的情况下提供深度信息。

Figure 5: Rendering RGB and Depth: Since depth is a by-product of the Gaussian Splatting rasterization process, GaussGym also renders depth without increasing rendering time.

4.2.5. Optimizations for High-Throughput and Realism

To achieve its reported high throughput and enhance realism, GaussGym incorporates specific optimizations:

1. Decoupling Rendering from Control Rate:

   • In robot control, the proprioceptive control rate (how often the robot's internal state is updated and actions are computed) is often very high (e.g., 500 Hz).
   • However, visual sensing (camera frame rate) is typically much slower (e.g., $10-30 Hz$).
   • GaussGym decouples these frequencies: rendering occurs at the camera's true frame rate, which is usually slower than the control frequency. This yields additional speed-ups because the renderer isn't burdened with generating frames at the physics simulation's high update rate, while still providing high-fidelity visual input when needed by the policy.

2. Simulated Motion Blur:

   • To reduce the Sim2Real gap and improve visual fidelity for dynamic scenarios, GaussGym introduces a novel method to simulate motion blur.

   • This is achieved by rendering a small set of frames offset along the camera's velocity direction and then alpha-blending them into a single output image.

   • This technique produces realistic blur artifacts that are especially noticeable during rapid movements or sudden jolts (e.g., stair climbing), improving visual fidelity and robustness in transfer to the real world. Example motion blur sequences are shown in Appendix Figure 10.

     该图像是插图，展示了GaussGym在运动模糊模拟中的应用。左侧显示了有运动模糊的场景，右侧为没有运动模糊的场景。通过结合快门速度和相机速度向量，GaussGym能够在机器人步行时生成运动模糊效果。

Figure 10: GaussGym proposes a simple yet novel to simulate motion blur. Given the shutter speed and camera velocity vector, GaussGym alpha blends various frames along the direction of motion. The effect is pronounced in jerky motions, for example when the foot comes into contact with stairs.

4.2.6. Policy Learning Architecture (Figure 7)

For visual locomotion and navigation tasks, GaussGym employs a specific neural architecture that processes both visual and proprioceptive inputs.

该图像是示意图，展示了视觉运动控制的架构，其中LSTM编码器融合了自我感知和DinoV2 RGB特征。输出传递至3D转置卷积头，用于占用格网和地形预测，以及一个策略LSTM，输出高斯动作分布。

Figure 7: Architecture for Visual Locomotion: An LSTM encoder fuses proprioception with DinoV2 RGB features. Outputs feed into a 3D transpose conv head for occupancy and terrain prediction, and a policy LSTM that outputs Gaussian action distributions.

• Recurrent Encoder (LSTM):

  • At each timestep, proprioceptive measurements (e.g., joint positions, velocities, base angular velocity, projected gravity angle, swing phase) are concatenated with DinoV2 embeddings.
  • DinoV2 (Oquab et al., 2023) is a pre-trained visual encoder that extracts robust features from raw RGB frames.
  • These combined features are then passed through a Long Short-Term Memory (LSTM) network. The LSTM is chosen for its ability to process sequences and capture temporal dynamics, while also being efficient for fast inference speed on a robot, avoiding the computational cost of vanilla transformer architectures.
  • The LSTM outputs a compact latent representation that encodes both temporal dynamics and visual semantics.

• Task-Specific Heads: Two distinct heads operate on this shared latent representation:

  1. Voxel Prediction Head:

     • The latent vector is unflattened into a coarse 3D grid.
     • This grid is then processed by a 3D transposed convolutional network.
     • Successive transposed convolution layers upscale this grid into a dense volumetric prediction of occupancy (whether a space is filled) and terrain heights.
     • The purpose of this head is to force the shared latent representation to explicitly capture the geometry of the scene from visual inputs. This acts as an auxiliary loss guided by ground-truth mesh data, significantly improving learning speed and performance for geometry-sensitive tasks like stair climbing.

  2. Policy Head:

     • A second LSTM consumes the latent representation along with its recurrent hidden state.
     • This LSTM outputs the parameters of a Gaussian distribution over joint position offset actions. This means the policy learns to predict adjustments to the robot's joint angles, allowing for precise control.

4.2.7. Rewards and Observations

The paper details the reward functions and observation space used for training the locomotion and navigation policies. These are standard components in RL that shape the agent's behavior.

The following are the results from Table 6 of the original paper:

Observations: The agent observes a combination of proprioceptive states and a visual input:

• $Base Ang Vel ωb$: Angular velocity of the robot's base.

• $Projected Gravity Angle α$: The angle of the robot's "up" vector relative to the global gravity vector, indicating orientation.

• Joint Positions q: The current angles of the robot's joints.

• Joint Velocities q: The current angular velocities of the robot's joints.

• $Swing phase φ$: A variable indicating the current stage of the robot's gait cycle.

• Image I (640 × 480): The RGB image input from the robot's camera, processed by DinoV2.

  The following are the results from Table 3 of the original paper:

  Reward	Expression	Weight
  Ang Vel XY	|ω|²	-0.2
  Orientation	||α∥k2	-0.5
  Action Rate	kqt − qt−1k2	-1.0
  Pose Deviation	|qt − k|2	-0.5
  Feet Distance	(fleft, xy − fright, xy) < 0.1	-10.0
  Feet Phase	1f , contact × φ ≤ 0.25	5.0
  Stumble	kFf ,xy ≥ 2kFf ,zk	-3.0

General Reward Terms (for all tasks): These terms encourage stable and efficient locomotion.

• Ang Vel XY: Penalizes large angular velocities in the XY plane (i.e., unwanted rotation). $ R_{AngVelXY} = -0.2 \times |\omega|^2 $ where $|\omega|$ is the magnitude of the angular velocity vector.
• Orientation: Penalizes deviation from the desired upright orientation. $ R_{Orientation} = -0.5 \times ||\alpha||_2 $ where $\alpha$ is the angle between the global up vector and the policy up vector.
• Action Rate: Penalizes large changes in actions between successive time steps, promoting smooth movements. $ R_{ActionRate} = -1.0 \times ||q_t - q_{t-1}||_2 $ where $q_t$ is the commanded action (joint angle) at time $t$ and $q_{t-1}$ is the commanded action at time t-1.
• Pose Deviation: Penalizes deviation of current joint angles from a reference pose (e.g., a standing pose). $ R_{PoseDeviation} = -0.5 \times |q_t - k|^2 $ where $q_t$ is the current joint angle and $k$ is a reference joint angle.
• Feet Distance: Penalizes feet being too close together (encourages stable stance). $ R_{FeetDistance} = -10.0 \times \mathbb{I}((f_{left, xy} - f_{right, xy}) < 0.1) $ where $f_{left, xy}$ and $f_{right, xy}$ are the 2D positions of the left and right feet, and $\mathbb{I}(\cdot)$ is the indicator function, which is 1 if the condition is true, 0 otherwise. This reward applies a large penalty if the horizontal distance between feet is less than 0.1 units.
• Feet Phase: Rewards appropriate foot contact during specific phases of the gait cycle. $ R_{FeetPhase} = 5.0 \times \mathbb{I}_{f, contact} \times \mathbb{I}(\phi \le 0.25) $ where $\mathbb{I}_{f, contact}$ is an indicator function that is 1 if the foot is in contact, and $\phi$ is the current gait phase. This rewards contact during the first quarter of the swing phase.
• Stumble: Penalizes excessive horizontal forces during foot contact, indicating stumbling. $ R_{Stumble} = -3.0 \times \mathbb{I}(||F_{f,xy}|| \ge 2||F_{f,z}||) $ where $F_{f,xy}$ is the horizontal component of the foot contact force and $F_{f,z}$ is the vertical component. This penalizes if the horizontal force is twice the vertical force, indicating a slip or stumble.

The following are the results from Table 4 of the original paper:

Velocity Tracking Task Rewards: These terms specifically reward the robot for matching desired linear and angular velocities.

• Linear Velocity Tracking: Rewards the agent for matching a desired linear velocity in the XY plane. $ R_{LinearVelocityTracking} = 1.0 \times \exp\left(-\frac{||v_{xy} - v^*_{xy}||^2}{0.25}\right) $ where $v_{xy}$ is the current linear velocity in the XY plane and $v^*_{xy}$ is the desired linear velocity. The exponential function ensures a higher reward when velocities are closer.
• Angular Velocity Tracking: Rewards the agent for matching a desired yaw rate (angular velocity around the Z-axis). $ R_{AngularVelocityTracking} = 0.5 \times \exp\left(-\frac{|\omega_z - \omega^*_z|^2}{0.25}\right) $ where $\omega_z$ is the current yaw rate and $\omega^*_z$ is the desired yaw rate.

The following are the results from Table 5 of the original paper:

Goal Tracking Task Rewards: These terms incentivize the robot to reach a target position and orientation.

• Position tracking: Rewards the agent for getting close to a target position. $ R_{PositionTracking} = 10.0 \times \mathbb{I}(t < 1) \times (1 - 0.5 \times ||r_{xy} - r^*_{xy}||) $ where $t$ is the remaining time to reach the goal, $r_{xy}$ is the current base position in the XY plane, and $r^*_{xy}$ is the desired base position. The reward is active only if $t < 1$ (likely for the final approach) and is higher when the robot is closer to the target.
• Yaw tracking: Rewards the agent for aligning its orientation with a target yaw. $ R_{YawTracking} = 10.0 \times \mathbb{I}(t < 1) \times (1 - 0.5 \times ||\psi - \psi^*||) $ where $\psi$ is the current base yaw and $\psi^*$ is the desired base yaw. Similar to position tracking, this reward is active near the goal and is higher for better alignment.

5. Experimental Setup

5.1. Datasets

GaussGym emphasizes its ability to ingest and process data from a wide range of sources, enabling the creation of diverse and realistic training environments.

• Sources of Data:
  • Smartphone Scans: Casual captures using common mobile devices. These provide real-world environments with relative ease.
  • Posed Datasets:
    • ARKitScenes (Baruch et al., 2021): A diverse real-world dataset for 3D indoor scene understanding using mobile RGB-D data. This provides structured indoor environments.
    • GrandTour (Frey et al., 2025): This dataset provides high-quality scans of large areas, offering complex and extensive outdoor or large-scale indoor environments. An example from GrandTour is shown in Figure 9.
  • Generative Video Model Outputs:

    • Veo (Google DeepMind, 2025): A generative video model capable of synthesizing photorealistic, multi-view consistent video. This source is particularly innovative, allowing GaussGym to create environments that are difficult or impossible to capture in the real world, such as fantasy worlds, disaster zones, or extraterrestrial terrains. Figure 4 illustrates examples of such generative environments.

      该图像是图示，展示了机器人在稀疏目标跟踪任务中穿越障碍物场景。中间的盒子标示了惩罚区域，RGB训练的策略（绿色）能识别并避开黄色地面，而仅依赖深度信息的策略（紫色）则无法检测，直接走过该区域。这突出表明RGB提供了超越几何深度的语义线索。

Figure 9: Large photorealistic worlds: GaussGym incorporates open-source datasets, such as GrandTour (Frey et al., 2025), which contains high quality scans of large areas. Shown above is a $2 0 \\mathrm { m ^ { 2 } }$ GaussGym scene derived from GrandTour, including the mesh (purple) and robot POV renders.

• Why these datasets were chosen: The diversity of these data sources (real-world scans, large-scale scientific datasets, and synthetic generative data) is crucial for:
  • Realism and Generalizability: Training RL policies in environments that closely resemble the real world helps reduce the sim-to-real gap and improve transfer performance.
  • Scalability: The framework allows for the incorporation of thousands of scenes, which is vital for large-scale robot learning and training robust policies that generalize across varied conditions.
  • "Beyond Reality" Training: Generative models enable the creation of novel or extreme environments that would be impractical or dangerous to train in, pushing the boundaries of policy robustness.

5.2. Evaluation Metrics

The paper evaluates policies based on their performance in specific tasks, implicitly using metrics like task success rate or performance scores (normalized to a maximum of 100 for some tables). For each reward term, the weight associated indicates its importance in shaping the policy's behavior during training. While explicit mathematical formulas for composite "success rates" are not provided, the reward functions themselves serve as the direct optimization target and thus reflect the criteria for successful behavior.

• Conceptual Definition:

  • Task Success Rate (Implicit in Table 2): Quantifies the percentage of trials or episodes in which the robot successfully completes a given task (e.g., reaching a goal, climbing stairs without falling) under specified conditions. A higher percentage indicates better performance and robustness.
  • Reward Accumulation: The sum of instantaneous rewards received by the agent over an episode. During training, the goal is to maximize this cumulative reward. The specific reward terms (Tables 3, 4, 5) define what constitutes "good" behavior.

• Mathematical Formula and Symbol Explanation: The paper does not provide a single overarching formula for "task success rate" or a normalized performance score. Instead, it details the reward functions that guide policy learning, which implicitly define success. For example, for the velocity tracking task, the policy is rewarded for matching desired linear and angular velocities (Table 4). For the goal tracking task, it's rewarded for proximity to the goal and correct orientation (Table 5), in addition to general locomotion stability (Table 3) and avoiding penalty regions (demonstrated in Figure 8).

  The reward expressions and their symbols were explained in detail in Section 4.2.7. Here's a brief recap of the reward components:

  • Angular Velocity XY ($|\omega|^2$): Penalizes unwanted base rotation.
  • Orientation ($||\alpha||_2$): Penalizes deviation from upright.
  • Action Rate ($||q_t - q_{t-1}||_2$): Penalizes abrupt action changes.
  • Pose Deviation ($|q_t - k|^2$): Penalizes deviation from desired joint configuration.
  • Feet Distance: Penalizes feet being too close, uses indicator function $\mathbb{I}(\cdot)$.
  • Feet Phase: Rewards foot contact during specific gait phase, uses indicator function $\mathbb{I}(\cdot)$.
  • Stumble: Penalizes excessive horizontal foot forces, uses indicator function $\mathbb{I}(\cdot)$.
  • Linear Velocity Tracking ($e^{-||v_{xy} - v^*_{xy}||^2/0.25}$): Rewards matching desired linear velocity.
    • $v_{xy}$: current linear velocity in XY plane.
    • $v^*_{xy}$: desired linear velocity in XY plane.
  • Angular Velocity Tracking ($e^{-|\omega_z - \omega^*_z|^2/0.25}$): Rewards matching desired yaw rate.
    • $\omega_z$: current yaw rate.
    • $\omega^*_z$: desired yaw rate.
  • Position Tracking ($1 - 0.5||r_{xy} - r^*_{xy}||$): Rewards proximity to target position.
    • $r_{xy}$: current base position in XY plane.
    • $r^*_{xy}$: desired base position in XY plane.
    • $\mathbb{I}(t < 1)$: Indicator for remaining time.
  • Yaw Tracking ($1 - 0.5||\psi - \psi^*||$): Rewards alignment with target yaw.
    • $\psi$: current base yaw.
    • $\psi^*$: desired base yaw.
    • $\mathbb{I}(t < 1)$: Indicator for remaining time.

5.3. Baselines

The paper primarily compares its vision-based policies against two main types of baselines, along with internal ablation studies to understand the contribution of different components:

• Geometric/Proprioceptive Baselines:

  • Blind policies: These policies rely purely on proprioceptive inputs (internal robot state) and potentially geometric inputs like depth maps or heightmaps, but not RGB visual semantics. In the context of visual navigation (Figure 8), a depth-only policy serves as a baseline to highlight the benefits of RGB. For locomotion, blind policies represent the standard approach that doesn't leverage pixel-level visual information (Table 2).
  • Comparison: The goal is to show that GaussGym-trained policies, by leveraging RGB, can outperform these baselines, especially in tasks requiring semantic reasoning or precise interaction with visually defined terrains.

• Internal Ablation Baselines (from Table 2): These studies systematically remove or modify components of the proposed vision-based policy to understand their individual contributions.

  • Vision w/o voxel: A policy trained with RGB input but without the auxiliary loss for voxel prediction (i.e., not explicitly reconstructing geometry from vision). This tests the importance of guiding the visual features towards geometric understanding.
  • Vision w/o DINO: A policy trained with RGB input but without using the pre-trained DinoV2 encoder for extracting visual features. This assesses the impact of robust visual embeddings.
  • Vision 10 scenes / Vision 50 scenes: Policies trained with RGB input but on a reduced number of scenes (10% and 50% respectively) compared to the full 2500 scenes. This investigates the importance of scene diversity for generalization and performance.

• Simulator Comparison (from Table 1): While not direct policy training baselines, GaussGym is implicitly compared against other simulation frameworks like LucidSim, LeVerb, and IsaacLab in terms of capabilities, especially photorealism, temporal consistency, FPS, renderer, and scene creation. This comparison establishes GaussGym's superiority as a platform for developing vision-based RL policies.

6. Results & Analysis

6.1. Core Results Analysis

The experimental results demonstrate GaussGym's effectiveness in enabling photorealistic simulation for vision-based robot learning, showcasing benefits in locomotion and navigation tasks, and highlighting the importance of rich visual semantics.

6.1.1. Training Environments Beyond Reality

GaussGym's ability to integrate data from generative video models like Veo (Google DeepMind, 2025) is a significant advancement. As illustrated in Figure 4 (examples of "fantasy world" and "Blade Runner-esque street" environments) and Figure 9 (large GrandTour scene), this allows for the creation of thousands of diverse and photorealistic environments that are difficult or impossible to capture in the real world. This capability drastically expands the diversity of training data for RL policies, which is crucial for generalization and robustness. The strong multi-view consistency of Veo and the robust camera estimation and dense point cloud generation of VGGT are key enablers for this.

6.1.2. Visual Locomotion and Navigation

The paper evaluates GaussGym by training locomotion and navigation policies for humanoid and quadrupedal robots directly from RGB pixels, without relying on multi-stage student-teacher distillation.

• Visual Stair Climbing:

  • Policies trained in GaussGym for a Unitree A1 robot using RGB input demonstrate highly precise foot placement on stairs and gait adaptation to avoid colliding with stair risers in simulation. This behavior, shown in Figure 6a and Appendix Figure 11, indicates that the policy learns to infer safe footholds directly from vision.

  • The A1 robot learns to lead with its front foot when approaching stairs, taking a large step to land securely on the second step. This suggests visual reasoning for complex terrain negotiation.

  • Zero-shot Sim-to-Real Transfer: As a proof of concept, the RGB locomotion policy trained in GaussGym successfully transfers to the real world for stair climbing without additional fine-tuning (Figure 6b). This is a crucial step towards closing the visual sim-to-real gap.

    该图像是示意图，展示了基于仿真学习的机器人在楼梯上的行走能力。图中显示了多个机器人沿楼梯逐步行进，强调它们在不同环境中的导航能力。

(a) RGB policy pre-trained in GaussGym. (b) Zero-shot deployment to real. Figure 6: Sim-to-real: GaussGym worlds enable training vision policies that transfer to real without fine-tuning.

该图像是示意图，展示了A1机器人在模拟中行走时的足部摆动轨迹。前脚（红色）和后脚（蓝色）的轨迹表明，A1能正确放置脚步，避免在楼梯边缘绊倒。接近楼梯时，A1前脚领先，大步踏下，确保安全着陆。

Figure 11: A1 foot swing trajectory: Foot trajectories for the visual locomotion policy in sim. The A1 learns to correctly place its front (red) and hind (blue) feet without stumbling on the stair edge. When approaching the stairs, A1 leads with the front foot, taking a large step to land securely in the middle of the second step, indicating that safe footholds can be directly inferred from vision.

• Visual Navigation and Semantic Reasoning:

  • In a sparse goal tracking task with an obstacle field, policies were tested for their ability to navigate and avoid undesirable regions. A yellow patch on the floor was designated as a penalty region.

  • As depicted in Figure 8, the RGB-trained policy (green trajectory) successfully perceived and avoided the yellow patch.

  • In contrast, a depth-only policy (purple trajectory) failed to detect the semantic meaning of the yellow patch and walked directly through it.

  • This result emphatically demonstrates that RGB input provides rich semantic cues beyond geometric depth, enabling policies to reason about environmental semantics and make more sophisticated decisions.

    该图像是图示，展示了机器人在稀疏目标跟踪任务中穿越障碍物场景。中间的盒子标示了惩罚区域，RGB训练的策略（绿色）能识别并避开黄色地面，而仅依赖深度信息的策略（紫色）则无法检测，直接走过该区域。这突出表明RGB提供了超越几何深度的语义线索。

Figure 8: Semantic reasoning from RGB: In the sparse goal tracking task, the robot must cross an obstacle field where a yellow floor patch incurs penalties. The RGB-trained policy (green) perceives and avoids the patch, while the depth-only policy (purple) cannot detect it and walks through. This highlights how RGB provides semantic cues beyond geometric depth.

6.2. Data Presentation (Tables)

The following are the results from Table 1 of the original paper:

†: Vectorized across 4096 envs on RTX4090. ‡: Vectorized across 768 envs on RTX4090.

This table, discussed in Section 3.4, highlights GaussGym's superior throughput and scene creation flexibility compared to other simulators, while maintaining photorealism and temporal consistency.

The following are the results from Table 2 of the original paper:

This table details the performance of different policy configurations (Full Vision, Blind, and various ablations) on different terrains (Flat, Steep, Short Stairs, Tall Stairs) for two robots (A1 and T1). Performance is likely measured as a success rate or a normalized score, with higher values indicating better performance.

The following are the results from Table 3 of the original paper:

This table lists the general reward terms and their weights used across all tasks to promote stable and efficient locomotion.

The following are the results from Table 4 of the original paper:

This table outlines the specific reward terms and weights used for the velocity tracking task, aiming to match desired linear and angular velocities.

The following are the results from Table 5 of the original paper:

This table specifies the reward terms and weights for the goal tracking task, encouraging the robot to reach a target position and orientation.

6.3. Ablation Studies / Parameter Analysis

Table 2 presents a large-scale ablation study on various design parameters, comparing the full Vision policy against several degraded versions and a Blind policy across four simulation scenarios (flat, steep, short stairs, tall stairs) for two robots (A1 and T1).

• Vision vs. Blind Policies:

  • On flat terrains, the Blind policy performs almost as well as Vision (e.g., A1: 98.1 vs. 100.0), indicating that RGB input provides minimal additional benefit when the terrain is simple.
  • However, on more challenging terrains (steep, short stairs, tall stairs), Vision policies consistently and significantly outperform Blind policies. For tall stairs, Vision (A1: 94.4, T1: 92.5) achieves much higher performance than Blind (A1: 74.0, T1: 60.5). This strongly validates the necessity and effectiveness of visual input for complex locomotion tasks where geometry needs to be inferred or precise interactions are required.

• Impact of Voxel Prediction Head (Vision w/o voxel):

  • Removing the auxiliary task of voxel prediction (Vision w/o voxel) generally reduces performance, especially on stairs. For A1 on tall stairs, performance drops from 94.4 to 80.8.
  • This indicates that explicitly forcing the latent representation to learn scene geometry by predicting occupancy and terrain heights is beneficial for learning speed and performance, helping the policy better understand the physical environment from RGB.

• Impact of DinoV2 Encoder (Vision w/o DINO):

  • Training without the pre-trained DinoV2 encoder (Vision w/o DINO) also reduces performance across challenging scenarios. For A1 on tall stairs, it drops from 94.4 to 88.3.
  • This highlights the importance of robust, pre-trained visual features extracted by DinoV2. These features provide a richer, more generalizable understanding of the visual world compared to learning features from scratch within the RL loop alone.

• Impact of Scene Diversity (Vision 10 scenes, Vision 50 scenes):

  • Training on a reduced number of scenes (10% or 50% of the full dataset) leads to a significant reduction in performance, particularly on the most difficult tall stairs scenario.

  • For A1 on tall stairs, performance drops from 94.4 (full scenes) to 67.3 (10 scenes) and 83.9 (50 scenes).

  • This finding emphasizes the relevance of seamless infrastructure (like GaussGym) to train across multiple diverse scenes. Large-scale scene diversity is crucial for developing generalizable and robust policies that can handle unforeseen variations in real-world environments.

    In summary, the full Vision policy, leveraging RGB input, auxiliary geometry reconstruction, and robust visual features from DinoV2 trained on a large number of diverse scenes, demonstrates the best performance across all challenging locomotion and navigation scenarios.

7. Conclusion & Reflections

7.1. Conclusion Summary

GaussGym introduces an innovative open-source framework that revolutionizes real-to-sim robot learning by integrating 3D Gaussian Splatting (3DGS) into vectorized physics simulators like IsaacGym. This integration achieves unprecedented simulation speeds (over 100,000 steps per second) while maintaining high photorealistic fidelity, enabling the training of visual locomotion and navigation policies directly from RGB pixels. The framework simplifies the creation of diverse training environments by ingesting data from smartphone scans, large-scale datasets, and generative video models. The research demonstrates that policies trained in GaussGym exhibit vision-perceptive behavior in simulation, including precise foothold placement and semantic reasoning (e.g., avoiding undesirable regions), and show promising zero-shot transfer to real-world scenarios. GaussGym serves as a critical open baseline, advancing scalable and generalizable robot learning by bridging the gap between high-throughput simulation and high-fidelity perception.

7.2. Limitations & Future Work

The authors acknowledge several limitations of GaussGym and areas for future research:

• Visual Sim-to-Real Transfer Challenges: While GaussGym shows promising zero-shot transfer, visual sim-to-real transfer remains a difficult and largely unsolved problem. The precise foot placement observed in simulation declined in real-world transfer, indicating further generalization experiments are needed across a broader set of tasks.
• Real-World Delays and Egocentric Observations: Transfer to real hardware introduces issues like physical delays (e.g., image latency) and the reliance on egocentric observations, which are simpler for geometry-based methods using elevation maps and high-frequency state estimation.
• Automated Reward Function Generation: GaussGym currently lacks automated mechanisms for generating cost or reward functions based on visual information, which is critical for tasks like adhering to social norms (e.g., walking on a crosswalk). The authors suggest that foundational language models could potentially help define these functions, an area for future work.
• Uniform Physical Parameters: Assets in GaussGym are currently initialized with uniform physical parameters (e.g., friction). This prevents accurate simulation of varied surfaces like ice, mud, or sand, limiting the connection between visual appearance and physical properties.
• Limitations of Current Vision Models: GaussGym inherits limitations from underlying vision models. Veo's outputs can be inconsistent and offer limited camera control. Future work could integrate more controllable and temporally consistent world models like Genie 3 (DeepMind, 2025).
• Dynamic Scenes and Deformable Assets: The current methodology for generating worlds from video models cannot yet handle dynamic scenes or simulate fluids and deformable assets beyond the rigid-body physics provided by IsaacGym.

7.3. Personal Insights & Critique

GaussGym represents a significant leap forward in the field of robot learning, particularly by directly tackling the visual sim-to-real gap. The integration of 3D Gaussian Splatting into vectorized physics simulators is a brilliant technical decision, as it capitalizes on the strengths of both, offering an unprecedented balance of photorealism and throughput. This work has the potential to democratize access to high-fidelity simulation environments, much like Isaac Gym did for geometric locomotion learning.

One of the most compelling aspects is the seamless incorporation of diverse real-world data and generative AI outputs. Leveraging video generation models like Veo to create "beyond reality" training environments opens up vast possibilities for training robots in scenarios that are difficult or impossible to replicate physically. The demonstration of semantic reasoning in the goal-reaching task is a strong testament to the value of RGB input over depth-only sensing, showcasing how robots can learn more nuanced decision-making.

Potential Issues/Areas for Improvement:

• Real-World Latency and Synchronization: While GaussGym decouples rendering from control rate in simulation, the latency of RGB camera streams and visual processing on real robots remains a practical challenge for high-frequency control. Future work might explore how to explicitly model or mitigate these latencies within the sim-to-real transfer pipeline.
• Automated Reward Engineering: The reliance on hand-crafted cost terms for RL is a common bottleneck. As noted by the authors, integrating foundational language models to dynamically generate or refine rewards based on semantic understanding of the scene (e.g., "avoid puddles," "stay on the path") would be a powerful extension.
• Dynamic Objects and Agent-Environment Interaction: The current framework excels at static scene representation. However, real-world environments are inherently dynamic, with moving obstacles, other agents, and deformable objects. Extending GaussGym to robustly handle dynamic 3DGS scenes (perhaps by integrating dynamic NeRF/3DGS techniques) and interaction with deformable bodies would be crucial for more complex real-world tasks.
• Physical Properties from Vision: The limitation of uniform physical parameters is significant. Learning to infer slippery surfaces or soft ground directly from visual cues and incorporating this into the physics simulation would further reduce the sim-to-real gap and enable more robust locomotion.

Transferability: The methodology of combining 3DGS with vectorized physics is highly transferable beyond locomotion. It could be applied to:

• Robotics Manipulation: Simulating photorealistic manipulation tasks with complex textures and lighting, allowing robots to learn dexterous manipulation from visual feedback.

• Autonomous Driving: Creating highly realistic and diverse urban or off-road driving scenarios for training perception and control systems.

• Human-Robot Interaction: Simulating complex social environments where robots need to interpret human cues and navigate among people, leveraging the semantic richness of RGB inputs.

  Overall, GaussGym is an exciting and foundational contribution that paves the way for a new generation of vision-based robot learning research.