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首页> 外文会议>International Conference on Robotics and Automation >Learning Robust Manipulation Strategies with Multimodal State Transition Models and Recovery Heuristics
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Learning Robust Manipulation Strategies with Multimodal State Transition Models and Recovery Heuristics

机译：通过多模式状态转移模型和恢复启发式算法学习鲁棒的操纵策略

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Robots are prone to making mistakes when performing manipulation tasks in unstructured environments. Robust policies are thus needed to not only avoid mistakes but also to recover from them. We propose a framework for increasing the robustness of contact-based manipulations by modeling the task structure and optimizing a policy for selecting skills and recovery skills. A multimodal state transition model is acquired based on the contact dynamics of the task and the observed transitions. A policy is then learned from the model using reinforcement learning. The policy is incrementally improved by expanding the action space by generating recovery skills with a heuristic. Evaluations on three simulated manipulation tasks demonstrate the effectiveness of the framework. The robot was able to complete the tasks despite multiple contact state changes and errors encountered, increasing the success rate averaged across the tasks from 70.0% to 95.3%.
机译：在非结构化环境中执行操纵任务时，机器人容易犯错误。因此，需要强有力的政策，不仅要避免错误，还要从错误中恢复过来。我们提出了一个框架，用于通过对任务结构进行建模并优化选择技能和恢复技能的策略来提高基于接触的操作的鲁棒性。基于任务的接触动力学和观察到的过渡，获取多峰状态过渡模型。然后使用强化学习从模型中学习策略。通过产生启发式恢复技能来扩大行动空间，从而逐步改善该策略。对三个模拟操作任务的评估证明了该框架的有效性。尽管有多个接触状态更改和遇到错误，该机器人仍能够完成任务，从而使整个任务的平均成功率从70.0％提高到95.3％。

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