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: 1700696
• 负责人: Sergey Levine
• 金额: $ 50万
• 依托单位: University of California-Berkeley
• 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 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1700696&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.

辅助机器人有可能通过帮助他们进行基本的日常活动，例如操纵物体和喂食来改变上肢残疾人的生活。但是，人类对辅助机器人的控制提出了重大挑战。机器人臂的高维度意味着类似操纵杆的界面很难直观地使用，而直接远距离作用的动作通常很慢，不精确，并且在其敏捷性方面受到严重限制。这项研究通过为共享自主权开发学习算法来解决这些挑战，该算法可以预期用户的意图，并提供一定程度的辅助自主权，以确保流体和成功的动作。这项研究还将为未来的研究铺平道路，该研究可以从远程流动引导并朝着完全的机器人自主权发展。该研究提出了一种使用深度学习和强化学习的技术，提出了一种分层和多个共享自治的方法。系统首先使用深层加固学习来快速确定用户的高级目标，例如用户是否要从原始感觉输入中掌握特定对象或操作设备。该目标推理层为较低控制层提供目标，该层由深度神经网络控制策略组成，这些策略可以直接处理有关环境和用户做出决策的原始感官输入。这些策略选择低级控件以满足高级目标，同时最大程度地减少与用户命令的分歧。该算法将在轮椅上的机器人臂上进行部署和测试，并有可能帮助上肢障碍用户进行日常生活活动。