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

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，还是年龄增长的共同影响，各种身体和精神残疾，以及每个人对它们不同的心理反应，都使得为辅助机器人编写一刀切的行为变得不可能。为了充分发挥其潜力，辅助机器人必须学会匹配所提供的援助的类型和程度，以满足用户的残疾程度和偏好，以及用户的环境和用户与机器人之间的信任程度。因此，训练机器人以适应个人用户是必不可少的，但要求所有用户训练机器人行为的所有方面是不现实的。在这个涉及两个机构教职员工的合作项目中，PI们认为，一种可能的解决方案可能源于这样的观察，即每当用户需要训练机器人适应新的行为时，很可能还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+机器人配对中的行为、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，将行为从一个人+机器人配对转移到另一个可能涉及机器人的代码和数据的转移和/或向人类用户的技能转移)。这项研究将展示人与机器人的混合交互如何改变用户与环境之间的关系，同时也使机器人与人类之间的物理交互更加安全和高效。这项工作将对全国产生广泛的影响，因为预计未来几年老年人口部分将迅速增长。私人投资经理将努力实现他们的愿景。他们将设计自适应算法和控制器(例如，用于滑动规模的机器人自主性)，使机器人在日常生活活动(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与机器人之间的信任模型，并检查信任对用户体验的影响。他们将实施社会代理，通过其社交网络，特定残疾用户社区可以帮助创建和采用针对ADL任务的新解决方案。他们还将验证人与机器人之间的交流能力，以提高残疾人ADL的功能和性能。这项研究将建立在机器人控制、社会学习的心理模型、社会网络模型以及协作过滤和推荐的机器学习技术的最新进展的基础上。项目成果将包括创建能够代表用户互动、发现学习机会并积极参与学习转移的社会代理。这项工作将有助于我们理解用户如何单独和集体地与辅助机器人合作，并将回答与在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有关的公开问题。

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.