11 papers
Impact of Different Failures on a Robot's Perceived Reliability
Andrew Violette, Zhanxin Wu, Haruki Nishimura +5
Robots fail, potentially leading to a loss in the robot's perceived reliability (PR), a measure correlated with trustworthiness. In this study we examine how various kinds of failu…
Physically-Feasible Reactive Synthesis for Terrain-Adaptive Locomotion
Ziyi Zhou, Qian Meng, Hadas Kress-Gazit +1
We present an integrated planning framework for quadrupedal locomotion over dynamically changing, unforeseen terrains. Existing methods often depend on heuristics for real-time foo…
A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation
TRI LBM Team, Jose Barreiros, Andrew Beaulieu +79
Robot manipulation has seen tremendous progress in recent years, with imitation learning policies enabling successful performance of dexterous and hard-to-model tasks. Concurrently…
Report on NSF Workshop on Science of Safe AI
Rajeev Alur, Greg Durrett, Hadas Kress-Gazit +2
Recent advances in machine learning, particularly the emergence of foundation models, are leading to new opportunities to develop technology-based solutions to societal problems. H…
Physically-Feasible Reactive Synthesis for Terrain-Adaptive Locomotion via Trajectory Optimization and Symbolic Repair
Ziyi Zhou, Qian Meng, Hadas Kress-Gazit +1
We propose an integrated planning framework for quadrupedal locomotion over dynamically changing, unforeseen terrains. Existing approaches either rely on heuristics for instantaneo…
INPROVF: Leveraging Large Language Models to Repair High-level Robot Controllers from Assumption Violations
Qian Meng, Jin Peng Zhou, Kilian Q. Weinberger +1
This paper presents INPROVF, an automatic framework that combines large language models (LLMs) and formal methods to speed up the repair process of high-level robot controllers. Pr…