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，帮助他们执行基本的日常活动，如操纵物体和喂食。然而，人类对辅助机器人的控制带来了巨大的挑战。机械臂的高维度意味着操纵杆状的界面不自然，很难直观地使用，而直接遥操作产生的运动往往缓慢、不精确，并且其灵活性受到严重限制。这项研究通过开发共享自主学习算法来解决这些挑战，在共享自主学习算法中，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，这些研究可以从遥操作中引导，并朝着完全机器人自主的方向发展。本研究利用深度学习和强化学习的方法，提出了一种层次化、多阶段的共享自主学习方法。该系统首先使用深度逆强化学习来从原始的感觉输入快速确定用户的高级目标，例如用户是否想要抓住特定对象或操作电器。该目标推理层向下层控制层提供目标，控制层由深度神经网络控制策略组成，可以直接处理关于环境和用户决策的原始感觉输入。这些策略选择低级控制来满足高级目标，同时最大限度地减少与用户命令的不一致。这些算法将在轮椅上安装的机械臂上进行部署和测试，有可能帮助上肢残疾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.