使用参数化偏置对运动风格施加额外约束的模仿学习
Imitation Learning with Additional Constraints on Motion Style using Parametric Bias
AI总结：
针对模仿学习中机器人运动风格依赖演示数据且易收敛为平均风格的问题，本研究在传统模仿学习网络中加入参数化偏置以约束运动风格，经PR2和MusashiLarm实验验证可通过多类约束按需改变运动风格完成任务。
摘要：
模仿学习是让机器人自适应复现人类演示的方法之一。此前研究发现，模仿学习的泛化能力可使机器人在未经训练的环境中自适应执行任务。然而，运动轨迹、施力大小等运动风格很大程度上依赖人类演示数据集，最终会收敛为平均运动风格。本研究提出一种方法，在传统模仿学习网络中加入参数化偏置，能够对运动风格施加约束。通过使用PR2机器人和肌肉骨骼人形机器人MusashiLarm开展实验，我们证明该方法可通过约束关节速度、肌肉长度速度和肌肉张力，按预期改变运动风格来执行任务。
英文摘要：
Imitation learning is one of the methods for reproducing human demonstration adaptively in robots. So far, it has been found that generalization ability of the imitation learning enables the robots to perform tasks adaptably in untrained environments. However, motion styles such as motion trajectory and the amount of force applied depend largely on the dataset of human demonstration, and settle down to an average motion style. In this study, we propose a method that adds parametric bias to the conventional imitation learning network and can add constraints to the motion style. By experiments using PR2 and the musculoskeletal humanoid MusashiLarm, we show that it is possible to perform tasks by changing its motion style as intended with constraints on joint velocity, muscle length velocity, and muscle tension.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
