Decoupling Skill Learning from Robotic Control for Generalizable Object Manipulation

• URL: http://arxiv.org/abs/2303.04016v2
• Date: Thu, 9 Mar 2023 07:14:20 GMT
• Title: Decoupling Skill Learning from Robotic Control for Generalizable Object Manipulation
• Authors: Kai Lu, Bo Yang, Bing Wang, Andrew Markham
• Abstract summary: Recent works in robotic manipulation have shown potential for tackling a range of tasks. We conjecture that this is due to the high-dimensional action space for joint control. In this paper, we take an alternative approach and separate the task of learning 'what to do' from 'how to do it' The whole-body robotic kinematic control is optimized to execute the high-dimensional joint motion to reach the goals in the workspace.
• Score: 35.34044822433743
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent works in robotic manipulation through reinforcement learning (RL) or imitation learning (IL) have shown potential for tackling a range of tasks e.g., opening a drawer or a cupboard. However, these techniques generalize poorly to unseen objects. We conjecture that this is due to the high-dimensional action space for joint control. In this paper, we take an alternative approach and separate the task of learning 'what to do' from 'how to do it' i.e., whole-body control. We pose the RL problem as one of determining the skill dynamics for a disembodied virtual manipulator interacting with articulated objects. The whole-body robotic kinematic control is optimized to execute the high-dimensional joint motion to reach the goals in the workspace. It does so by solving a quadratic programming (QP) model with robotic singularity and kinematic constraints. Our experiments on manipulating complex articulated objects show that the proposed approach is more generalizable to unseen objects with large intra-class variations, outperforming previous approaches. The evaluation results indicate that our approach generates more compliant robotic motion and outperforms the pure RL and IL baselines in task success rates. Additional information and videos are available at https://kl-research.github.io/decoupskill

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