A Data-Efficient Progressive Learning Framework for Robot Scooping Task

Abstract

Robot scooping is a challenging and important task in robotic tool manipulation research due to the complex relationship between the robot, the tool, and target objects/environment. Taking into account different tools, different target objects and varying environments, the required scooping manipulation strategy usually varies greatly. Even considering a specific type of spoon, the question of how to obtain a policy model that requires less demonstration data but shows better generalization capabilities deserves further exploration. In this paper, we propose a progressive learning framework for general robot scooping tasks, which requires a limited number of demonstrations but shows promising generalization capability. We first learn a scooping policy via human demonstrations with a specific setup. We then use this as a pre-train model for reinforcement learning in a curriculum manner to achieve a scooping strategy that is generalizable to different task setups. Finally, we evaluate the capabilities of the policy with a series of experiments both in simulation and on a real robot.