Observe Then Act: Asynchronous Active Vision-Action Model for Robotic Manipulation

• URL: http://arxiv.org/abs/2409.14891v2
• Date: Tue, 1 Oct 2024 15:31:23 GMT
• Title: Observe Then Act: Asynchronous Active Vision-Action Model for Robotic Manipulation
• Authors: Guokang Wang, Hang Li, Shuyuan Zhang, Yanhong Liu, Huaping Liu,
• Abstract summary: Our model serially connects a camera Next-Best-View (NBV) policy with a gripper Next-Best Pose (NBP) policy, and trains them in a sensor-motor coordination framework using few-shot reinforcement learning. This approach allows the agent to adjust a third-person camera to actively observe the environment based on the task goal, and subsequently infer the appropriate manipulation actions. The results demonstrate that our model consistently outperforms baseline algorithms, showcasing its effectiveness in handling visual constraints in manipulation tasks.
• Score: 13.736566979493613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In real-world scenarios, many robotic manipulation tasks are hindered by occlusions and limited fields of view, posing significant challenges for passive observation-based models that rely on fixed or wrist-mounted cameras. In this paper, we investigate the problem of robotic manipulation under limited visual observation and propose a task-driven asynchronous active vision-action model.Our model serially connects a camera Next-Best-View (NBV) policy with a gripper Next-Best Pose (NBP) policy, and trains them in a sensor-motor coordination framework using few-shot reinforcement learning. This approach allows the agent to adjust a third-person camera to actively observe the environment based on the task goal, and subsequently infer the appropriate manipulation actions.We trained and evaluated our model on 8 viewpoint-constrained tasks in RLBench. The results demonstrate that our model consistently outperforms baseline algorithms, showcasing its effectiveness in handling visual constraints in manipulation tasks.

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