1. Bibliographic Information

• Title: Learning to See and Act: Task-Aware View Planning for Robotic Manipulation
• Authors: Yongjie Bai, Zhouxia Wang, Yang Liu, Weixing Chen, Ziliang Chen, Mingtong Dai, Yongsen Zheng, Lingbo Liu, Guanbin Li, and Liang Lin.
  • Affiliations: The authors are from various prestigious institutions, including Sun Yat-sen University, Pengcheng Laboratory, Nanyang Technological University, and Shenzhen Institutes of Advanced Technology (Chinese Academy of Sciences). This indicates a collaborative effort from strong academic and research labs in the field of AI and robotics.
  • Note: Yongjie Bai and Zhouxia Wang are marked as having equal contributions.
• Journal/Conference: The paper is available on arXiv, which is a preprint server. This means it has not yet undergone formal peer review for publication in a journal or conference. The provided source link (https://arxiv.org/abs/2508.05186) appears to be a fictional placeholder, as the year "2508" is in the future.
• Publication Year: 2508 (as per the fictional source link).
• Abstract: The abstract identifies two key limitations in current vision-language-action (VLA) models for robotic manipulation: (1) reliance on static camera viewpoints, which causes perception issues like occlusion, and (2) the use of shared visual encoders for multiple tasks, which leads to task interference. To address this, the authors propose Task-Aware View Planning (TAVP). TAVP combines an active view planning policy (to find the most informative camera angles) with a task-specific representation learning method using a Mixture-of-Experts (MoE) visual encoder. The goal is to generate more complete and discriminative visual features, leading to better action prediction. Experiments on the RLBench benchmark show that TAVP significantly outperforms state-of-the-art fixed-view approaches.
• Original Source Link:
  • Official Source: https://arxiv.org/abs/2508.05186 (Note: This is a fictional link provided in the prompt).
  • PDF Link: https://arxiv.org/pdf/2508.05186v2.pdf (Note: This is a fictional link provided in the prompt).
  • Publication Status: The paper is a preprint and has not yet been formally peer-reviewed.

2. Executive Summary

• Background & Motivation (Why):

  • Core Problem: Modern robots trained to perform multiple tasks often fail in complex or cluttered environments. This is because they typically rely on a few fixed cameras, which can lead to crucial objects or the robot's own gripper being hidden (occluded). This incomplete visual information results in incorrect or failed actions.
  • Existing Gaps:
    1. Static Viewpoints: Most Vision-Language-Action (VLA) models use static cameras, which are insufficient for understanding 3D space fully. As shown in Figure 1, a fixed camera might see the target cupboard but not the sugar the robot is holding, or vice versa.
    2. Task Interference: When a single neural network is trained on many different tasks (e.g., "open a drawer" vs. "pick an apple"), the features learned for one task can conflict with those for another. This "task interference" limits the model's ability to generalize and scale to more tasks.
  • Innovation: The paper introduces a new framework, Task-Aware View Planning (TAVP), which teaches a robot to "learn to see" before it "learns to act". Instead of passively receiving images, the robot actively decides where to look to get the best possible view for the specific task it is performing.

• Main Contributions / Findings (What):

  1. Multi-Viewpoint Exploration Policy (MVEP): A novel policy that enables the robot to actively explore and select the most informative camera viewpoints for a given task. This dynamic re-rendering of views effectively solves problems caused by occlusions and limited fields of view, enhancing the robot's 3D perception.
  2. Task-aware Mixture-of-Experts (TaskMoE): An advanced visual encoder that uses a set of specialized "expert" neural networks. Guided by the task instruction and visual scene, it dynamically routes the input to the most relevant experts. This disentangles the learning process for different tasks, reducing interference and improving multi-task performance and generalization.
  3. Superior Robotic Manipulation Performance: Through extensive experiments on 18 challenging tasks in the RLBench simulator and on a real-world robot, TAVP is shown to significantly outperform existing state-of-the-art methods that use fixed viewpoints.

3. Prerequisite Knowledge & Related Work

• Foundational Concepts:

  • Vision-Language-Action (VLA) Models: These are end-to-end models designed for robotics that take multiple types of input—vision (camera images), language (text instructions like "put the sugar in the cupboard"), and sometimes proprioception (the robot's own state)—to directly output an action (e.g., gripper movement). They streamline the traditional perception-planning-control pipeline into a single model.
  • Multi-task Learning: The practice of training a single model to perform multiple, often distinct, tasks. The paper discusses two approaches:
    • Modular: Breaking down skills into separate modules (perception, planning, etc.). This is interpretable but scales poorly.
    • End-to-end: Using a single, large model to map perception directly to action. This is simpler but can suffer from task interference, where learning one task negatively impacts performance on another.
  • Mixture-of-Experts (MoE): A neural network architecture that consists of numerous "expert" sub-networks and a "gating" network. For any given input, the gating network selects a small subset of experts to process it. This allows the model to have a very large number of parameters (many specialized experts) while keeping the computational cost for each input low (sparse activation).
  • Reinforcement Learning (RL): A machine learning paradigm where an "agent" (the robot) learns to make decisions by interacting with an "environment." It learns a policy (a strategy for choosing actions) by receiving rewards or penalties for its actions, aiming to maximize its total cumulative reward over time.
  • Point Cloud: A set of 3D data points in space, typically captured by depth sensors (like RGB-D cameras). Point clouds represent the 3D structure of a scene and are used in this paper to create a complete 3D model of the environment, from which new camera views can be rendered.

• Previous Works & Differentiation:

  • The paper situates itself within the context of VLA models like RVT, PerAct, and ARP. However, it criticizes them for their reliance on fixed viewpoints, which is the primary problem TAVP aims to solve with its MVEP.
  • It also acknowledges prior work using MoE in robotics, such as SDP. However, it differentiates its TaskMoE by introducing two key innovations:
    1. Richer Routing: Instead of just using a task ID, TaskMoE uses a combination of the language instruction and visual information to select experts, making the routing more context-aware.
    2. Decoupled Gating: It uses fewer gates than the total number of tasks ($N_G < N_J$), forcing semantically similar tasks to share routing pathways. This encourages the model to learn underlying task similarities and improves generalization to unseen tasks.
  • Regarding RL, the paper avoids the complexity of training from scratch by using RL to fine-tune a pre-trained model. It introduces a novel pseudo-environment that calculates rewards based on the performance of a fixed-view model, which is much more efficient than interacting with the physical simulator for every training step.

4. Methodology (Core Technology & Implementation)

The core of the paper is the Task-Aware View Planning (TAVP) framework, which integrates active view selection with task-specific feature extraction. The overall pipeline is shown in Figure 2.

该图像是论文中用于展示Task-Aware View Planning（TAVP）框架的示意图，描述了从多视角观察输入到粗略定位、细致重渲染以及通过TaskMoE和自回归策略生成动作的流程，体现了多视角探索策略与任务特定视觉编码的结合。

• Principles: The fundamental idea is to first "see" the world in a task-aware manner by actively finding the best viewpoints, and then "act" based on this enhanced visual information. This is achieved by combining a viewpoint exploration policy (MVEP) with a specialized multi-task encoder (TaskMoE).

• Steps & Procedures:

  1. Input & 3D Reconstruction: The model receives a language instruction (e.g., "close the jar"), RGB-D images from initial cameras, and the robot's gripper state. These initial images are used to reconstruct a 3D point cloud of the entire scene.
  2. Coarse Grounding & Area of Interest: Following the RVT-2 approach, the model first performs a coarse prediction to identify a general "area of interest" for the task.
  3. Task-Aware Feature Extraction (TaskMoE): A Multi-View Transformer (MVT) enhanced with the proposed TaskMoE module processes the scene. TaskMoE routes the task instruction and visual data to specialized experts, ensuring the extracted features are highly relevant to the specific task.
  4. Viewpoint Exploration (MVEP): The MVEP policy takes the task-aware features and the 3D point cloud as input and predicts the parameters for $K$ optimal camera poses. These poses are chosen to maximize the visibility of task-relevant objects and the end-effector.
  5. Re-rendering & Fine-Grained Prediction: The scene is re-rendered from these $K$ newly selected viewpoints to generate new, more informative images. These images are fed into a second TaskMoE-enhanced MVT and an action prediction model to generate the final, precise 6-DoF action (position, rotation), gripper state, and collision prediction.

• Mathematical Formulas & Key Details:

  1. Task-aware Mixture-of-Experts (TaskMoE) As illustrated in Figure 3, TaskMoE is designed to handle diverse tasks without interference.

  • Context-Aware Routing: It uses a cross-attention mechanism to fuse features from the language Instruction and Vision (scene). The resulting features are modulated by the Task ID using a Feature-wise Linear Modulation (FiLM) layer. This rich, contextual information is fed into the gating network.

  • Decoupled Gating: It employs $N_G$ gates for $N_J$ tasks, where $N_G < N_J$. This forces tasks with similar semantics or action patterns (e.g., "open top drawer" and "open bottom drawer") to share a gate, promoting knowledge sharing and generalization. Tasks that are very different (e.g., "open drawer" vs. "stack blocks") are routed through different gates. For each input, the top-$k$ experts out of a total of $N_E$ are activated.

    

    2. Multi-Viewpoint Exploration Policy (MVEP) The MVEP is a neural network that predicts optimal camera poses.

  • Input: The input is the concatenated 3D point cloud coordinates $\mathcal{P}$ and their associated RGB color features $\mathbf{F}_{\text{img}}$. $$\mathbf{X} = \mathrm{Concat}(\mathcal{P}, \mathbf{F}_{\text{img}}) \in \mathbb{R}^{N \times 6}$$

  • Pose Parameterization: Each camera pose is defined by a 5D vector $\mathbf{p}^i = (\theta^i, \phi^i, r^i, \theta_{\text{up}}^i, \phi_{\text{up}}^i)$, representing its position in spherical coordinates $(\theta, \phi, r)$ and its up-vector orientation $(\theta_{\text{up}}, \phi_{\text{up}})$. The camera always looks at the origin.

  • Probabilistic Output: Instead of predicting deterministic poses, MVEP outputs the mean $\mu^i$ and log-standard deviation $\log\sigma^i$ of a Gaussian distribution for each of the 5 parameters. A specific camera pose $\tilde{\mathbf{p}}^i$ is then sampled using the reparameterization trick, which allows gradients to flow back through the sampling process: $$\tilde{\mathbf{p}}^i = \mu^i + \sigma^i \odot \epsilon^i, \quad \epsilon^i \sim \mathcal{N}(\mathbf{0}, \mathbf{I})$$

    • Symbol Explanation:
      • $\tilde{\mathbf{p}}^i$: The sampled 5D pose vector for the $i$-th viewpoint.
      • $\mu^i, \sigma^i$: The mean and standard deviation of the Gaussian distribution for the pose parameters, predicted by the network.
      • $\epsilon^i$: A random noise vector sampled from a standard normal distribution.
      • $\odot$: Element-wise product.

  • Output Normalization: The sampled values are passed through a sigmoid function $\sigma(\cdot)$ to ensure they fall within valid physical ranges (e.g., angles between 0 and $\pi$ or $2\pi$, radius between $r_{\text{min}}$ and $r_{\text{max}}$).

  3. Three-Stage Training Strategy

  • Stage 1: Pre-training: A fixed-view version of TAVP is trained using behavior cloning on expert demonstrations. The loss function is a sum of losses for different action components: $$\mathcal{L}_{\mathrm{s}1} = \mathcal{L}_{hc} + \mathcal{L}_{hf} + \mathcal{L}_{rot} + \mathcal{L}_{gri} + \mathcal{L}_{col}$$
    • Symbol Explanation: $\mathcal{L}_{hc}$ / $\mathcal{L}_{hf}$ (coarse/fine heatmap loss), $\mathcal{L}_{rot}$ (rotation loss), $\mathcal{L}_{gri}$ (gripper state loss), $\mathcal{L}_{col}$ (collision prediction loss).
  • Stage 2: MVEP Training with RL: The pre-trained model is frozen. Only the MVEP is trained using the Proximal Policy Optimization (PPO) algorithm. To avoid slow simulation interaction, a pseudo-environment provides rewards based on how much the MVEP-selected views improve the action prediction loss compared to the fixed-view model. The total reward is a weighted sum of three components:
    1. Task Loss Improvement ($r_0$): $r_0 = \mathcal{L}_{\text{ref}} - \mathcal{L}_{\text{TAVP}}$. This rewards views that lead to a lower action prediction loss than the fixed-view reference model.
    2. Confidence Reward ($r_1$): $r_1 = - \frac{1}{K} \sum_{i=1}^K \mathcal{H}(\text{softmax}(\mathbf{H}_i))$. This rewards views that produce high-confidence (low entropy) localization heatmaps.
    3. Viewpoint Diversity ($r_2$): $r_2 = \frac{1}{K(K-1)} \sum_{i \neq j} (1 - \cos(\mathbf{p}_i, \mathbf{p}_j))$. This encourages the policy to select diverse, non-redundant camera positions.
  • Stage 3: End-to-End Fine-tuning: The MVEP policy is frozen, and the rest of the TAVP model is fine-tuned on the views provided by MVEP. This adapts the action prediction network to the new, dynamic viewpoints.

5. Experimental Setup

• Datasets:

  • Simulation: The RLBench benchmark, which uses the CoppeliaSim simulator and a Franka Emika Panda robot. Experiments are run on 18 different manipulation tasks (e.g., Open Drawer, Stack Cups, Place Wine).

  • Real World: A setup with a 6-DoF Dobot Nova 2 robot and three Intel RealSense depth cameras. Five custom tasks were created: Pick Grape, Stack Bowls, Push Buttons, Collect Fruits, and Put Item In Drawer. 50 expert demonstrations were collected for each task.

    该图像是论文中的图4，展示了真实环境中的机器人操作设置。图中包含了左右机械臂及多个深度相机（D455、D435i、D405），并展示了不同相机视角下的拍摄效果，体现了多视角数据采集的实验环境。

• Evaluation Metrics:

  • Success Rate: The primary metric used to evaluate performance.
    1. Conceptual Definition: It measures the percentage of trials in which the robot successfully completes the entire task according to predefined criteria (e.g., the block is placed in the correct location, the drawer is fully opened). It is a direct measure of task-level effectiveness.
    2. Mathematical Formula: $$\text{Success Rate} (\%) = \frac{\text{Number of Successful Trials}}{\text{Total Number of Trials}} \times 100$$
    3. Symbol Explanation:
       • Number of Successful Trials: The count of test episodes where the robot achieved the task goal.
       • Total Number of Trials: The total number of test episodes attempted for a given task.

• Baselines: The paper compares TAVP against several state-of-the-art models in robotic manipulation, including:

  • Fixed-View VLA Models: RVT2, ARP, and $ARP+$. These are the most direct competitors as they are dense, end-to-end models but use fixed camera viewpoints.
  • Other 3D-based Models: C2F-ARM-BC, PerAct, HiveFormer, PolarNet, Act3D, and 3D Diffuser Actor.
  • Real-World Baseline: Diffusion Policy (DP), a well-known imitation learning method.

6. Results & Analysis

• Core Results:

  • RLBench Performance: The main results are presented in Table 1. TAVP achieves an average success rate of 86.6% across 18 RLBench tasks. This is the highest score, outperforming the strongest fixed-view baseline, $ARP+$, which scored 84.9%. The performance gains are particularly large on tasks highly susceptible to occlusion, such as Put in Cupboard (TAVP: 74.0% vs. ARP+: 69.6%) and Insert Peg (not shown in this snippet but mentioned in the text with a +56% improvement). This confirms that active view planning is crucial for solving tasks where visibility is a challenge.

    (Manual transcription of Table 1, as no image was provided)

    Method	Avg. Success	Close Jar	Drag Stick	Insert Peg	Meat off Grill	Open Drawer	Place Cups	Place Wine	Push Buttons
    C2F-ARM-BC [178] (CVPR)	20.1	24.0	24.0	4.0	20.0	20.0	0.0	8.0	72.0
    PerAct [157] (CoRL)	49.4	55.2±4.7	89.6±4.1	5.6±4.1	70.4±2.0	88.0±5.7	2.4±3.2	44.8±7.8	92.8±3.0
    HiveFormer [183] (CoRL)	45.0	52.0	76.0	0.0	80.0	52.0	0.0	80.0	84.0
    PolarNet [186] (CoRL)	46.0	36.0	92.0	4.0	100.0	84.0	0.0	40.0	96.0
    RVT [155] (CoRL)	62.9	52.0±2.5	99.2±1.6	11.2±3.0	88.0±2.5	71.2±6.9	4.0±2.5	91.0±5.2	100.0±0.0
    Act3D [184] (CoRL)	63.2	96.8±3.0	80.8±6.4	24.0±8.4	95.2±1.6	78.4±11.2	3.2±3.0	59.2±9.3	93.6±2.0
    3D Diffuser Actor [185] (CoRL)	81.3	96.0±2.5	100.0±0.0	65.6±4.1	96.8±1.6	89.6±4.1	24.0±7.6	93.6±4.8	98.4±2.0
    RVT2 [143] (RSS)	81.4	100.0±0.0	99.0±1.7	40.0±0.0	99.0±1.0	74.0±11.8	38.0±4.5	95.0±3.3	100.0±0.0
    ARP [79] (RA-L)	81.6	97.6	88.0	53.2	96.0	90.4	48.0	92.0	100.0
    ARP+ [79] (RA-L)	84.9	95.2	99.2	78.4	97.6	92.8	48.8	96.0	100.0
    TAVP(Ours)	86.6	100.0±0.0	100.0±0.0	98.0±2.8	94.0±2.8	90.0±2.8	54.0±2.8	92.0±5.7	100.0±0.0
    	Put in Cupboard	Put in Drawer	Put in Safe	Screw Bulb	Slide Block	Sort Shape	Stack Blocks	Stack Cups	Sweep to Dustpan	Turn Tap
    C2F-ARM-BC [178] (CVPR)	0.0	4.0	12.0	8.0	16.0	8.0	0.0	0.0	0.0	68.0
    PerAct [157] (CoRL)	28.0±4.4	51.2±4.7	84.0±3.6	17.6±2.0	74.0±13.0	16.8±4.7	26.4±3.2	2.4±2.0	52.0±0.0	88.0±4.4
    HiveFormer [183] (CoRL)	32.0	68.0	76.0	8.0	64.0	12.0	4.0	0.0	28.0	80.0
    PolarNet [186] (CoRL)	12.0	32.0	84.0	44.0	56.0	12.0	8.0	8.0	52.0	80.0
    RVT [155] (CoRL)	49.6±3.2	88.0±5.7	91.2±3.0	48.0±5.7	81.6±5.4	36.0±2.5	28.8±3.9	26.4±8.2	72.0±0.0	94.4±2.0
    Act3D [184] (CoRL)	67.2±3.0	91.2±6.9	97.6±2.0	32.8±6.9	96.0±2.5	29.6±3.2	4.0±3.6	9.6±6.0	86.4±6.5	99.2±1.6
    3D Diffuser Actor [185] (CoRL)	85.6±4.1	96.0±3.6	95.2±4.0	82.4±2.0	97.6±3.2	44.0±4.4	68.3±3.3	47.2±8.5	84.0±4.4	93.6±4.1
    RVT2 [143] (RSS)	66.0±4.5	96.0±0.0	96.0±2.8	88.0±4.9	92.0±2.8	35.0±2.8	80.0±2.8	69.0±5.9	100.0±0.0	99.0±1.7
    ARP [79] (RA-L)	68.0	99.2	94.4	85.6	98.4	35.2	55.2	76.8	90.4	100.0
    ARP+ [79] (RA-L)	69.6	98.4	86.4	89.6	92.8	46.4	63.2	80.0	97.6	96.0
    TAVP(Ours)	74.0±8.5	100.0±0.0	78.0±2.8	86.0±2.8	100.0±0.0	62.0±8.5	74.0±2.8	64.0±5.7	92.0±5.7	100.0±0.0

  • Real-World Performance: In real-world tests (Table 4), TAVP achieves an average success rate of 88.0%, a 20 percentage point improvement over the Diffusion Policy baseline (68.0%). This demonstrates the method's effectiveness and adaptability outside of simulation.

    (Manual transcription of Table 4)

    Method / Task	Pick Grape	Stack Bowls	Push Buttons	Collect Fruits	Put Item In Drawer	Avg. Succ
    Diffusion Policy	90.0	70.0	70.0	50.0	60.0	68.0
    TAVP(Ours)	100.0	90.0	100.0	70.0	80.0	88.0

• Ablations / Parameter Sensitivity:

  • Component Ablation (Table 2): This study confirms the importance of each new component.

    • Removing TaskMoE causes a performance drop (86.67% -> 85.56%).

    • Replacing active exploration with random viewpoints causes a catastrophic failure (8.89% success rate), proving that intelligently selecting views is critical.

    • Using fixed viewpoints (the model from Stage 1) results in an 83.33% success rate. The jump to 86.67% shows the benefit of the full TAVP framework.

      (Manual transcription of Table 2)

      Configuration	Average Success Rate (%)
      TAVP	86.67
      w/o TaskMoE	85.56
      w/o Active Exploration Fine-tuning (Random Viewpoints)	8.89
      w/o Active Exploration Fine-tuning (Fixed Viewpoints)	83.33

  • Sensitivity Analysis (Table 3): This analysis reveals that performance is sensitive to MVEP's hyperparameters.

    • Number of Views (K): More views lead to better performance. Increasing from 2 to 4 views raises the average success rate from 27.2% to 55.2%.
    • Camera Radial Constraint (r): A tighter, more focused search space for the camera's distance from the target (0.90~1.04m) yields better results (56.0% success) than a looser one (0.60~1.56m, 48.8% success). This suggests that providing a good prior on viewing distance is beneficial.

  • Zero-shot Generalization (Table 6): This experiment highlights the generalization capability of TaskMoE.

    • On seen tasks, the model with TaskMoE significantly outperforms the one without (49.6% vs. 24.0% success).
    • Most importantly, on an unseen task ("Open drawer"), the model with TaskMoE achieves a 12.0% success rate, whereas the model without it fails completely (0.0%). This demonstrates that TaskMoE's ability to learn task similarities allows it to generalize to novel, out-of-distribution tasks.

  • Visualization Results (Figure 5): The visualizations provide compelling qualitative evidence. In both simulation and the real world, the baseline $ARP+$ fails because its fixed view leads to occlusion (e.g., it loses sight of the mug or misjudges the height of a plate). In contrast, TAVP actively selects new viewpoints that keep both the object and the target in frame, allowing it to complete the task successfully. This visually confirms that dynamic "seeing" enables robust "acting."

    该图像是论文中的示意图，展示了在模拟RLBench环境和真实环境下，TAVP与Baseline(ARP+)的多视角视觉输入及任务执行效果对比。上方为TAVP成功完成任务的连续视角画面，下方为Baseline失败的对应视角画面。

7. Conclusion & Reflections

• Conclusion Summary: The paper successfully demonstrates that integrating active, task-aware view planning with specialized multi-task representation learning can significantly overcome the limitations of traditional fixed-viewpoint robotic systems. The proposed TAVP framework, with its MVEP and TaskMoE modules, achieves superior performance and robustness on a wide array of manipulation tasks. The work validates the principle that enabling a robot to intelligently decide how to see is a critical step towards building more general-purpose and reliable manipulation systems.

• Limitations & Future Work: The authors acknowledge several limitations:

  • Inference Latency: The active view planning and re-rendering process introduces a slight computational overhead, increasing inference time.
  • Point Cloud Dependency: The method relies on accurate 3D point cloud reconstruction. This can be challenging in real-world scenarios with transparent or highly reflective objects that are difficult for depth cameras to capture correctly.
  • Future Directions: The authors suggest exploring multi-sensor fusion to improve real-world perception and domain adaptation techniques to enhance robustness.

• Personal Insights & Critique:

  • Strengths:
    • The core idea of actively seeking information is intuitive and powerful. It moves beyond passive perception and is a more anthropomorphic approach to problem-solving.
    • The three-stage training process, particularly the use of a "pseudo-environment" for RL in Stage 2, is a very clever and practical solution to make the training of the exploration policy efficient.
    • The TaskMoE design, with its cross-modal routing and decoupled gating, is a sophisticated approach to multi-task learning that shows clear benefits for generalization.
  • Potential Weaknesses & Open Questions:
    • Complexity: The three-stage training pipeline is complex and may be challenging to implement and tune compared to a single-stage end-to-end model.
    • Scalability of MVEP: While effective, the MVEP policy is trained to select from views rendered from a static point cloud. In highly dynamic environments where the scene changes rapidly, this approach might not be fast enough, as the point cloud would need constant updating.
    • Generalization of View Planning: The paper shows generalization for TaskMoE. It would be interesting to see how well the MVEP itself generalizes to new objects and environments that require completely different viewing strategies than those seen in training.