103 citations · 192 across the 31 of their papers we have counts for
5 papers · 1 filter
RoboMD: Uncovering Robot Vulnerabilities through Semantic Potential Fields
Som Sagar, Jiafei Duan, Sreevishakh Vasudevan +4
Robot manipulation policies, while central to the promise of physical AI, are highly vulnerable in the presence of external variations in the real world. Diagnosing these vulnerabi…
Repairing Neural Networks for Safety in Robotic Systems using Predictive Models
Keyvan Majd, Geoffrey Clark, Georgios Fainekos +1
This paper introduces a new method for safety-aware robot learning, focusing on repairing policies using predictive models. Our method combines behavioral cloning with neural netwo…
A Comparison of Imitation Learning Algorithms for Bimanual Manipulation
Michael Drolet, Simon Stepputtis, Siva Kailas +4
Amidst the wide popularity of imitation learning algorithms in robotics, their properties regarding hyperparameter sensitivity, ease of training, data efficiency, and performance h…
Achieving Human Level Competitive Robot Table Tennis
David B. D'Ambrosio, Saminda Abeyruwan, Laura Graesser +24
Achieving human-level speed and performance on real world tasks is a north star for the robotics research community. This work takes a step towards that goal and presents the first…
Task Success is not Enough: Investigating the Use of Video-Language Models as Behavior Critics for Catching Undesirable Agent Behaviors
Lin Guan, Yifan Zhou, Denis Liu +3
Large-scale generative models are shown to be useful for sampling meaningful candidate solutions, yet they often overlook task constraints and user preferences. Their full power is…