Learning to See and Act: Task-Aware View Planning for Robotic Manipulation

• URL: http://arxiv.org/abs/2508.05186v1
• Date: Thu, 07 Aug 2025 09:21:20 GMT
• 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, Liang Lin,
• Abstract summary: Task-Aware View Planning (TAVP) is a framework designed to integrate active view planning with task-specific representation learning.<n>Our proposed TAVP model achieves superior performance over state-of-the-art fixed-view approaches.
• Score: 85.65102094981802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent vision-language-action (VLA) models for multi-task robotic manipulation commonly rely on static viewpoints and shared visual encoders, which limit 3D perception and cause task interference, hindering robustness and generalization. In this work, we propose Task-Aware View Planning (TAVP), a framework designed to overcome these challenges by integrating active view planning with task-specific representation learning. TAVP employs an efficient exploration policy, accelerated by a novel pseudo-environment, to actively acquire informative views. Furthermore, we introduce a Mixture-of-Experts (MoE) visual encoder to disentangle features across different tasks, boosting both representation fidelity and task generalization. By learning to see the world in a task-aware way, TAVP generates more complete and discriminative visual representations, demonstrating significantly enhanced action prediction across a wide array of manipulation challenges. Extensive experiments on RLBench tasks show that our proposed TAVP model achieves superior performance over state-of-the-art fixed-view approaches. Visual results and code are provided at: https://hcplab-sysu.github.io/TAVP.

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