NRI: Collaborative Research: Learning Deep Sensorimotor Policies for Shared Autonomy

辅助机器人有可能改变上肢残疾人的生活，帮助他们完成基本的日常活动，比如操纵物体和喂食。然而，人类对辅助机器人的控制提出了实质性的挑战。机械臂的高维度意味着像操纵杆一样的界面是不自然的，很难直观地使用，直接远程操作产生的运动通常是缓慢的，不精确的，并且在灵巧性上受到严重限制。本研究通过开发共享自主的学习算法来解决这些挑战，其中机器人预测用户的意图并提供一定程度的辅助自主，以确保流畅和成功的运动。这项研究也将为未来的研究铺平道路，这些研究可以从远程操作开始，并朝着完全自主的方向发展。该研究提出了一种分层和多阶段的共享自治方法，使用了深度学习和强化学习的技术。该系统首先使用深度逆强化学习来快速确定用户的高级目标，例如用户是否想要从原始感官输入中抓取特定物体或操作设备。该目标推理层为下层控制层提供目标，下层控制层由深度神经网络控制策略组成，可以直接处理有关环境和用户的原始感官输入以做出决策。这些策略选择低级控制来满足高级目标，同时尽量减少与用户命令的不一致。这些算法将在安装在轮椅上的机械臂上进行部署和测试，该机械臂有可能帮助上肢残疾的用户进行日常生活活动。

Assistive robots have the potential to transform the lives of persons with upper extremity disabilities, by helping them perform basic daily activities, such as manipulating objects and feeding. However, human control of assistive robots presents substantial challenges. The high dimensionality of robotic arms means that joystick-like interfaces are unnatural hard to use intuitively, and motions resulting from direct teleoperation are often slow, imprecise, and severely limited in their dexterity. This research address these challenges by developing learning algorithms for shared autonomy, where the robot anticipates the user's intent and provides a degree of assistive autonomy to ensure fluid and successful motions. This research will also pave the way for future research that can bootstrap from teleoperation and build towards full robot autonomy. The research proposes a hierarchical and multi-phased approach to shared autonomy, using techniques from deep learning and reinforcement learning. The system begins by using deep inverse reinforcement learning to quickly ascertain the user's high-level goal, such as whether the user wants to grasp a particular object or operate an appliance, from raw sensory inputs. This goal inference layer supplies objectives to the lower control layer, which consists of deep neural network control policies that can directly process raw sensory input about the environment and the user to make decisions. These policies choose low-level controls to satisfy the high-level objective while minimizing disagreement with the user's commands. The algorithms will be deployed and tested on a wheelchair-mounted robot arm with the potential to assist users with upper extremity disabilities to perform activities of daily living.