Learning in-hand manipulation for a compliant underactuated gripper with interactive human supervision

本项目旨在设计和开发欠驱动机器人抓手，通过人体演示和交互反馈，学习适应性柔性操作可变形物体。在第一年，我们完成了第一个目标，开发了一个紧凑的多自由度机器人抓手，具有解耦的手腕，使每个关节的独立位置和力控制。我们进行了实验验证微创手术应用程序使用开发的夹具。我们收集了抓取不同刚度材料时的致动器信号，并训练了一个高斯过程回归模型用于无传感器抓取力预测。我们还设计了一个多目标逆运动学控制框架，可以同时处理多个约束下的关节位置控制。下一个目标是使用强化学习，模拟到真实的技术和交互式反馈从人体演示中学习自适应顺应性。我们计划开发一个模拟环境，可以捕获人类演示，并使用最先进的深度强化学习算法训练无模型策略。

This project aims to design and develop underactuated robotic grippers that can learn adaptive compliance for soft manipulation of deformable objects through human demonstrations and interactive feedback.In the first year, we accomplished the first objective of developing a compact multi-dof robotic gripper with a decoupled wrist that enables independent position and force control of each joint. We carried out experimental validation for minimally invasive surgical applications using the developed gripper. We collected actuator signals when grasping materials with different stiffness and trained a Gaussian process regression model for sensorless grip force prediction. We also devised a multi-objective inverse kinematics control framework that can handle joint position control under multiple constraints simultaneously.The next objective is to learn adaptive compliance from human demonstrations using reinforcement learning, sim-to-real techniques and interactive feedback. We plan to develop a simulation environment that can capture human demonstrations and train a model-free policy using state-of-the-art deep reinforcement learning algorithms.