通过手持式示教接口实现的基于观测的可行性感知模仿学习
Feasibility-aware Imitation Learning from Observations through a Hand-mounted Demonstration Interface
AI总结：
本文提出FABCO框架，利用机器人动力学模型评估示教可行性并以视觉反馈引导示教者改进，同时将可行性作为数据权重，在移液管插入任务中验证了其对数据效率和策略鲁棒性的提升作用。
摘要：
通过示教接口进行模仿学习，有望从直观的人类示教中学习机器人自动化策略。然而，由于人类与机器人运动特性存在差异，人类专家可能在无意中示教了机器人无法执行的动作。我们提出基于观测的可行性感知行为克隆（FABCO）。在FABCO框架中，利用机器人预训练的前向和逆向动力学模型评估每次示教的可行性。该可行性信息以视觉反馈的形式提供给示教者，鼓励其改进示教。在策略学习过程中，估计的可行性作为示教数据的权重，从而提升数据效率和所学策略的鲁棒性。我们通过将其应用于涉及移液管和小瓶的移液管插入任务，实验验证了FABCO的有效性。四名参与者评估了FABCO中可行性反馈和加权策略学习的影响。此外，我们使用NASA任务负荷指数（NASA-TLX）评估了带视觉反馈的示教所引发的工作负荷。
英文摘要：
Imitation learning through a demonstration interface is expected to learn policies for robot automation from intuitive human demonstrations. However, due to the differences in human and robot movement characteristics, a human expert might unintentionally demonstrate an action that the robot cannot execute. We propose feasibility-aware behavior cloning from observation (FABCO). In the FABCO framework, the feasibility of each demonstration is assessed using the robot's pre-trained forward and inverse dynamics models. This feasibility information is provided as visual feedback to the demonstrators, encouraging them to refine their demonstrations. During policy learning, estimated feasibility serves as a weight for the demonstration data, improving both the data efficiency and the robustness of the learned policy. We experimentally validated FABCO's effectiveness by applying it to a pipette insertion task involving a pipette and a vial. Four participants assessed the impact of the feasibility feedback and the weighted policy learning in FABCO. Additionally, we used the NASA Task Load Index (NASA-TLX) to evaluate the workload induced by demonstrations with visual feedback.
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。
