使用透视视觉触觉传感器的多模态与力匹配模仿学习
Multimodal and Force-Matched Imitation Learning with a See-Through Visuotactile Sensor
AI总结：
本研究在模仿学习框架中利用多模态视觉触觉传感器执行接触密集型任务，提出触觉力匹配和学习模式切换两种算法改进，实验证明其在四个开门任务中显著提升了策略成功率。
摘要：
接触密集型任务持续给机器人操作带来诸多挑战。在本研究中，我们在模仿学习（IL）框架内利用多模态视觉触觉传感器，执行涉及末端执行器与操作物体之间相对运动（如滑动和滑移）的接触密集型任务。我们引入了两项算法贡献：触觉力匹配和学习模式切换，作为改进IL的互补方法。触觉力匹配通过读取演示期间的近似力并生成重新创建记录力的适应机器人轨迹来增强动觉教学。学习模式切换使用IL将视觉和触觉传感器模式与学习的运动策略耦合，简化从接触到接触的过渡。我们在四个开门任务上进行了机器人操作实验，采用多种观测和算法配置，以研究多模态视觉触觉传感及我们提出的改进的效用。结果显示，力匹配使平均策略成功率提高62.5%，视觉触觉模式切换提高30.3%，视觉触觉数据作为策略输入提高42.5%，强调了透视触觉传感对IL的价值，既用于数据收集以允许力匹配，又用于策略执行以实现准确任务反馈。项目网站：https://papers.starslab.ca/sts-il/
英文摘要：
Contact-rich tasks continue to present many challenges for robotic manipulation. In this work, we leverage a multimodal visuotactile sensor within the framework of imitation learning (IL) to perform contact-rich tasks that involve relative motion (e.g., slipping and sliding) between the end-effector and the manipulated object. We introduce two algorithmic contributions, tactile force matching and learned mode switching, as complimentary methods for improving IL. Tactile force matching enhances kinesthetic teaching by reading approximate forces during the demonstration and generating an adapted robot trajectory that recreates the recorded forces. Learned mode switching uses IL to couple visual and tactile sensor modes with the learned motion policy, simplifying the transition from reaching to contacting. We perform robotic manipulation experiments on four door-opening tasks with a variety of observation and algorithm configurations to study the utility of multimodal visuotactile sensing and our proposed improvements. Our results show that the inclusion of force matching raises average policy success rates by 62.5%, visuotactile mode switching by 30.3%, and visuotactile data as a policy input by 42.5%, emphasizing the value of see-through tactile sensing for IL, both for data collection to allow force matching, and for policy execution to enable accurate task feedback. Project site: https://papers.starslab.ca/sts-il/
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。
