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

在不久的将来，辅助机器人有望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，还是年龄增长的常见影响，各种各样的身体和精神残疾，以及每个人对它们的不同心理反应，都使得为辅助机器人制定一刀切的行为程序变得不可能。为了充分发挥其潜力，辅助机器人必须学会根据用户的残疾程度和偏好，以及用户所处的环境和用户与机器人之间的信任程度，来匹配所提供帮助的类型和程度。因此，训练机器人以适应个人用户是必要的，但要求所有用户训练机器人行为的各个方面是不现实的。在这个涉及两所机构的教员的合作项目中，pi认为，一个可能的解决方案可能来自于这样的观察：每当用户需要训练机器人的新行为时，很可能有其他有类似残疾、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+机器人对行为的技术，识别新行为的可能受益者，并将这些行为转移给这些受益者（其中将行为从一个人+机器人对转移到另一个人可能涉及机器人代码和数据的转移和/或将技能转移给人类用户）。本研究将展示混合人机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之间的物理交互更安全、更高效。这项工作将对全国产生广泛的影响，因为预计未来几年老龄人口将迅速增长。为实现他们的愿景，私人党将采取四项措施。他们将设计自适应算法和控制器（例如，用于滑动尺度机器人自治），使机器人能够在日常生活活动（adl）中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，并研究信任对用户体验的影响。他们将实现社会代理，通过社交网络，特定残疾用户社区可以帮助为ADL任务创建和采用新的解决方案。他们将验证人与机器人交流的能力，以增加残疾人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同过滤和推荐的机器学习技术的最新进展之上。项目成果将包括创建能够代表用户进行交互、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，并将回答与一个学习系统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开放性问题。

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.