CHS: Medium: Collaborative Research: Social Learning in Mixed Human-Robot Groups for People with Disabilities

CHS：媒介：协作研究：残疾人混合人机群体的社会学习

• 批准号: 1409823
• 负责人: Aman Behal
• 金额: $ 106.2万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1409823&HistoricalAwards=false
• 关键词: CHS; Medium; Collaborative; Research; Social

Assistive robots promise to improve the lives of many people with disabilities in the near future. But whether due to traumatic spinal cord injury, early onset multiple sclerosis, or the common effects of advancing age, the variety of physical and mental disabilities, and the different psychological reaction of each individual to them, make it impossible to program one-size-fits-all behaviors for assistive robots. To achieve its full potential, the assistive robot must learn to match the type and degree of assistance offered to the disability level and preferences of the user, as well as to the user's environment and the level of trust between the user and the robot. Thus, training the robot to fit the individual user is essential - but requiring all users to train all aspects of robot behavior is unrealistic. In this collaborative project involving faculty at two institutions, the PIs argue that a possible solution may derive from the observation that whenever a user needs to train a robot for a new behavior, it is likely that there are other users with similar disabilities, preferences and environments who might also benefit from this behavior. The PIs will develop techniques which enable the learning of behaviors in human+robot pairs, the identification of possible beneficiaries of the new behaviors, and the transfer of these behaviors to these beneficiaries (where transferring a behavior from one human+robot pair to another might involve the transfer of code and data for the robot and/or the transfer of skills to the human user). This research will demonstrate how mixed human+robot interaction can alter the relationship between users and their environment, while also rendering physical interaction between robot and human safer and more efficient. The work will have broad national impact because of the expected rapid growth in coming years of the elderly segment of the population.The PIs will pursue four thrusts to achieve their vision. They will design adaptive algorithms and controllers (e.g., for sliding-scale robot autonomy) which allow a robot to be an effective facilitator of user interaction with novel environments during activities of daily living (ADLs). They will develop models of human+robot trust in the context of assistive robot technology, and examine the effect of trust on the user experience. They will implement social agents through which the community of users with a specific disability via their social networks can help in the creation and adoption of new solutions for ADL tasks. And they will validate the ability of human+robot exchanges to increase functionality and performance of ADLs for disabled individuals. The research will build on recent advances in robot control, psychological models of social learning, and models of social networks, as well as machine learning techniques of collaborative filtering and recommendation. Project outcomes will include the creation of social agents that can interact on behalf of the user, discover learning opportunities, and actively participate in the transfer of learning. The work will contribute to our understanding of how users can partner, both individually and collectively, with assistive robots, and will answer open questions relating to the interoperability and intelligibility of knowledge developed in one learning system to another.

辅助机器人有望在不久的将来改善许多残疾人的生活。 但无论是由于创伤性脊髓损伤、早发性多发性硬化症，还是由于年龄增长的共同影响，身体和精神残疾的多样性以及每个人对其的不同心理反应，都使得辅助机器人不可能制定一刀切的行为。 为了充分发挥其潜力，辅助机器人必须学会将所提供的辅助类型和程度与用户的残疾程度和偏好、用户的环境以及用户和机器人之间的信任程度相匹配。 因此，训练机器人以适应个人用户是至关重要的，但要求所有用户训练机器人行为的各个方面是不现实的。在这个涉及两个机构的教师的合作项目中，PI 认为，一个可能的解决方案可能源自这样的观察：每当用户需要训练机器人进行新行为时，很可能有其他具有类似残疾、偏好和环境的用户也可能从这种行为中受益。 PI 将开发能够学习人类+机器人对的行为、识别新行为的可能受益者以及将这些行为转移给这些受益者的技术（其中将行为从一对人类+机器人转移到另一对可能涉及为机器人转移代码和数据和/或将技能转移给人类用户）。 这项研究将展示人类+机器人的混合交互如何改变用户与其环境之间的关系，同时使机器人与人类之间的物理交互更安全、更高效。 由于预计未来几年老年人口将快速增长，这项工作将在全国范围内产生广泛影响。PI 将采取四项措施来实现他们的愿景。 他们将设计自适应算法和控制器（例如，用于滑动规模机器人自主），使机器人能够在日常生活活动（ADL）期间成为用户与新环境交互的有效促进者。 他们将在辅助机器人技术的背景下开发人类+机器人信任模型，并检查信任对用户体验的影响。 他们将实施社交代理，通过社交网络，具有特定残疾的用户社区可以帮助创建和采用 ADL 任务的新解决方案。 他们将验证人类+机器人交流的能力，以提高残疾人的 ADL 功能和性能。 该研究将建立在机器人控制、社交学习心理模型、社交网络模型以及协作过滤和推荐的机器学习技术方面的最新进展的基础上。 项目成果将包括创建可以代表用户进行交互、发现学习机会并积极参与学习迁移的社交代理。 这项工作将有助于我们理解用户如何与辅助机器人进行单独和集体合作，并将回答与一个学习系统中开发的知识与另一个学习系统中开发的知识的互操作性和可理解性相关的开放性问题。