A Knowledge Driven Approach to Adaptive Assistance Using Preference Reasoning and Explanation
Franklin & Marshall College（富兰克林与马歇尔学院）；Massachusetts Institute of Technology（麻省理工学院）；Occidental College（西方学院）
Jason R. Wilson, Leilani Gilpin, Irina Rabkina
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Accepted for presentation at the AAAI 2020 Fall Symposium Series, in the symposium for Artificial Intelligence for Human-Robot Interaction: Trust & Explainability in Artificial Intelligence for Human-Robot Interaction
There is a need for socially assistive robots (SARs) to provide transparency in their behavior by explaining their reasoning. Additionally, the reasoning and explanation should represent the user's preferences and goals. To work towards satisfying this need for interpretable reasoning and representations, we propose the robot uses Analogical Theory of Mind to infer what the user is trying to do and uses the Hint Engine to find an appropriate assistance based on what the user is trying to do. If the user is unsure or confused, the robot provides the user with an explanation, generated by the Explanation Synthesizer. The explanation helps the user understand what the robot inferred about the user's preferences and why the robot decided to provide the assistance it gave. A knowledge-driven approach provides transparency to reasoning about preferences, assistance, and explanations, thereby facilitating the incorporation of user feedback and allowing the robot to learn and adapt to the user.
Mobile telepresence robots (MTRs) have become increasingly popular in the expanding world of remote work, providing new avenues for people to actively participate in activities at a distance. However, humans operating MTRs often have difficulty navigating in densely populated environments due to limited situation awareness and narrow field-of-view, which reduces user acceptance and satisfaction. Shared autonomy in navigation has been studied primarily in static environments or in situations where only one pedestrian interacts with the robot. We present a multimodal shared autonomy approach, le…
Artificial intelligence technologies that can assist with at-home tasks have the potential to help older adults age in place. Robot assistance in particular has been applied towards physical and cognitive support for older adults living independently at home. Surveys, questionnaires, and group interviews have been used to understand what tasks older adults want robots to assist them with. We build upon prior work exploring older adults' task preferences for robot assistance through field interviews situated within older adults' aging contexts. Our findings support results from prior work indic…
It is important for socially assistive robots to be able to recognize when a user needs and wants help. Such robots need to be able to recognize human needs in a real-time manner so that they can provide timely assistance. We propose an architecture that uses social cues to determine when a robot should provide assistance. Based on a multimodal fusion approach upon eye gaze and language modalities, our architecture is trained and evaluated on data collected in a robot-assisted Lego building task. By focusing on social cues, our architecture has minimal dependencies on the specifics of a given …
Georgia Institute of Technology（佐治亚理工学院）；Institute for Robotics and Intelligent Machines（机器人与智能机器研究所）
With the growing capabilities of intelligent systems, the integration of artificial intelligence (AI) and robots in everyday life is increasing. However, when interacting in such complex human environments, the failure of intelligent systems, such as robots, can be inevitable, requiring recovery assistance from users. In this work, we develop automated, natural language explanations for failures encountered during an AI agents' plan execution. These explanations are developed with a focus of helping non-expert users understand different point of failures to better provide recovery assistance. …
Georgia Institute of Technology（佐治亚理工学院）；Stanford University（斯坦福大学）；Enway GmbH（Enway有限公司）
Autonomous robots have the potential to serve as versatile caregivers that improve quality of life for millions of people worldwide. Yet, conducting research in this area presents numerous challenges, including the risks of physical interaction between people and robots. Physics simulations have been used to optimize and train robots for physical assistance, but have typically focused on a single task. In this paper, we present Assistive Gym, an open source physics simulation framework for assistive robots that models multiple tasks. It includes six simulated environments in which a robotic ma…
Analyzing Reluctance to Ask for Help When Cooperating With Robots: Insights to Integrate Artificial Agents in HRC
Interactive Robotics Group, Massachusetts Institute of Technology（麻省理工学院交互机器人组）；Department of Aeronautics and Astronautics（航空与航天系）；Department of Autonomous and Intelligent Systems（自主与智能系统系）；Faculty of Informatics, UPV/EHU（信息学院）
Personalized Lower-limb Exoskeleton Assistance via Preference-based Bayesian Optimization
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Macau University of Science and Technology（澳门科技大学）
Explanations given by automation are often used to promote automation adoption. However, it remains unclear whether explanations promote acceptance of automated vehicles (AVs). In this study, we conducted a within-subject experiment in a driving simulator with 32 participants, using four different conditions. The four conditions included: (1) no explanation, (2) explanation given before or (3) after the AV acted and (4) the option for the driver to approve or disapprove the AV's action after hearing the explanation. We examined four AV outcomes: trust, preference for AV, anxiety and mental wor…
Prior studies have found that explaining robot decisions and actions helps to increase system transparency, improve user understanding, and enable effective human-robot collaboration. In this paper, we present a system for generating personalized explanations of robot path planning via user feedback. We consider a robot navigating in an environment modeled as a Markov decision process (MDP), and develop an algorithm to automatically generate a personalized explanation of an optimal MDP policy, based on the user preference regarding four elements (i.e., objective, locality, specificity, and cor…
When interacting in unstructured human environments, occasional robot failures are inevitable. When such failures occur, everyday people, rather than trained technicians, will be the first to respond. Existing natural language explanations hand-annotate contextual information from an environment to help everyday people understand robot failures. However, this methodology lacks generalizability and scalability. In our work, we introduce a more generalizable semantic explanation framework. Our framework autonomously captures the semantic information in a scene to produce semantically descriptive…
Toward Context-Aware Exoskeleton Assistance: Integrating Computer Vision Payload Estimation with a Multi-Metric Optimization Space
Department of Mechanical Engineering, Politecnico di Milano（米兰理工学院机械工程系）；Department of Biomedical Engineering, National University of Singapore（新加坡国立大学生物医学工程系）；School of Science and Technology, Singapore University of Social Sciences（新加坡社会科学院科学与技术学院）；College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学机电学院）
From Vision to Assistance: Gaze and Vision-Enabled Adaptive Control for a Back-Support Exoskeleton
Department of Innovative Technologies (DTI), Dalle Molle Institute for Artificial Intelligence (IDSIA), University of Applied Sciences and Arts of Southern Switzerland (SUPSI)（创新技术系（DTI）、人工智能研究所（IDSIA）、瑞士应用科学与艺术大学（SUPSI））；Department of Informatics, Systems and Communication (DISCo), University of Milano-Bicocca（信息、系统与通信系（DISCo）、米兰-比科卡大学）；Department of Mechanical Engineering, Politecnico di Milano（机械工程系、米兰理工学院）
Monash University（莫纳什大学）；University of Waterloo（滑铁卢大学）
When a robot learns from human examples, most approaches assume that the human partner provides examples of optimal behavior. However, there are applications in which the robot learns from non-expert humans. We argue that the robot should learn not only about the human's objectives, but also about their expertise level. The robot could then leverage this joint information to reduce or increase the frequency at which it provides assistance to its human's partner or be more cautious when learning new skills from novice users. Similarly, by taking into account the human's expertise, the robot wou…
