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Estimating People's Subjective Experiences of Robot Behavior

AAAI Conferences

Progress in general HRI metrics, while significant, is often in the form of post-experiment questionnaires or expert video analysis. This is a significant hurdle for any intelligent system that aspires to interact with its social environment. In the case of social navigation, robots must react to a dynamic environment. Socially aware robot behavior requires real time quantitative metrics of human subjective experience. This is a vast topic, which we approach by trying to measure how predictable robot actions are from its impact on the paths taken by passers-by. We chose predictability as a first metric because it can be modelled in terms of efficiency, and it affects safety. Thus, it serves as an interesting proof of concept.


Developing Effective Robot Teammates for Human-Robot Collaboration

AAAI Conferences

Developing collaborative robots that can productively operate out of isolation and work safely in uninstrumented, human-populated environments is critically important for advancing the field of robotics. Especially in domains where modern robots are ineffective, we wish to leverage human-robot teaming to improve the efficiency, ability, and safety of human workers. Our work, outlined in this extended abstract, focuses on creating agents capable of human-robot teamwork by leveraging learning from demonstration, hierarchical task networks, multi-agent planning and state estimation, and intention recognition. We briefly describe our recent work within human-robot collaboration, including task comprehension, learning and performing assistive behaviors, and training novice human collaborators to become competent co-workers.


Towards Enhancing Human-Robot Relationship: Customized Robot’s Behavior to Human’s Profile

AAAI Conferences

A social robot should be able to understand human’s profile (i.e., human’s emotions and personality), so as to make the robot able to behave appropriately to the multimodal interaction context. This research addresses the online recognition of emotions based on a new fuzzy-based methodology. It also focuses on investigating how could a match between the human’s and the robot’s personalities influence interaction. Furthermore, it studies the automatic generation of head-arm metaphoric gestures under different emotional states based on the prosodic cues of the interacting human. The conducted experiments have been validated with NAO robot from Aldebaran Robotics and ALICE robot from Hanson Robotics.


Behavior Trees as a Representation for Medical Procedures

arXiv.org Artificial Intelligence

Objective: Effective collaboration between machines and clinicians requires flexible data structures to represent medical processes and clinical practice guidelines. Such a data structure could enable effective turn-taking between human and automated components of a complex treatment, accurate on-line monitoring of clinical treatments (for example to detect medical errors), or automated treatment systems (such as future medical robots) whose overall treatment plan is understandable and auditable by human experts. Materials and Methods: Behavior trees (BTs) emerged from video game development as a graphical language for modeling intelligent agent behavior. BTs have several properties which are attractive for modeling medical procedures including human-readability, authoring tools, and composability. Results: This paper will illustrate construction of BTs for exemplary medical procedures and clinical protocols. Discussion and Conclusion: Behavior Trees thus form a useful, and human authorable/readable bridge between clinical practice guidelines and AI systems.


Behavioral Repertoires for Soft Tensegrity Robots

arXiv.org Artificial Intelligence

Mobile soft robots offer compelling applications in fields ranging from urban search and rescue to planetary exploration. A critical challenge of soft robotic control is that the nonlinear dynamics imposed by soft materials often result in complex behaviors that are counterintuitive and hard to model or predict. As a consequence, most behaviors for mobile soft robots are discovered through empirical trial and error and hand-tuning. A second challenge is that soft materials are difficult to simulate with high fidelity -- leading to a significant reality gap when trying to discover or optimize new behaviors. In this work we employ a Quality Diversity Algorithm running model-free on a physical soft tensegrity robot that autonomously generates a behavioral repertoire with no a priori knowledge of the robot dynamics, and minimal human intervention. The resulting behavior repertoire displays a diversity of unique locomotive gaits useful for a variety of tasks. These results help provide a road map for increasing the behavioral capabilities of mobile soft robots through real-world automation.