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

NRI：协作研究：学习共享自主权的深度感觉运动策略

• 批准号: 1637443
• 负责人: Sergey Levine
• 金额: $ 50万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2017-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637443&HistoricalAwards=false
• 关键词: NRI; Collaborative; Research; Learning; Deep

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.

辅助机器人有可能改变上肢残疾人的生活，帮助他们进行基本的日常活动，如操纵物体和喂食。然而，人类对辅助机器人的控制提出了重大挑战。机器人手臂的高维度意味着类似于鼠标的界面是不自然的，难以直观地使用，并且由直接遥控操作产生的运动通常是缓慢的，不精确的，并且在灵活性方面受到严重限制。这项研究通过开发共享自主的学习算法来解决这些挑战，机器人可以预测用户的意图，并提供一定程度的辅助自主性，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引导并建立完全的机器人自主性。该研究提出了一种分层和多阶段的共享自治方法，使用深度学习和强化学习技术。该系统首先使用深度反向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住特定对象或操作设备。这个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。这些策略选择低级别的控制来满足高级别的目标，同时最大限度地减少与用户命令的不一致。这些算法将在安装在轮椅上的机器人手臂上进行部署和测试，该机器人手臂有可能帮助上肢残疾的用户进行日常生活活动。