Reinforcement Learning for Active Search and Grasp in Clutter

This paper presents an Active Search policy that balances between moving the camera and removing occluding objects to search for and retrieve a target object in clutter. While both types of action can reveal unobserved parts of a scene, they typically vary in execution complexity and time. Our proposed method explicitly reasons about the occluded spaces in the scene where the target object may be hidden, and uses reinforcement learning to compute the value of each action with the ultimate goal of finding and retrieving the target object in minimal time. Results in simulation and real-world experiments demonstrate a significant improvement in both task execution speed and success rate compared to baseline grasping strategies.