31 citations · 178 across the 53 of their papers we have counts for
5 papers · 1 filter
ELEMENTAL: Interactive Learning from Demonstrations and Vision-Language Models for Reward Design in Robotics
Letian Chen, Nina Moorman, Matthew Gombolay
Reinforcement learning (RL) has demonstrated compelling performance in robotic tasks, but its success often hinges on the design of complex, ad hoc reward functions. Researchers ha…
Designs for Enabling Collaboration in Human-Machine Teaming via Interactive and Explainable Systems
Rohan Paleja, Michael Munje, Kimberlee Chang +2
Collaborative robots and machine learning-based virtual agents are increasingly entering the human workspace with the aim of increasing productivity and enhancing safety. Despite t…
Mixed-Initiative Human-Robot Teaming under Suboptimality with Online Bayesian Adaptation
Manisha Natarajan, Chunyue Xue, Sanne van Waveren +2
For effective human-agent teaming, robots and other artificial intelligence (AI) agents must infer their human partner's abilities and behavioral response patterns and adapt accord…
Efficient Trajectory Forecasting and Generation with Conditional Flow Matching
Sean Ye, Matthew Gombolay
Trajectory prediction and generation are crucial for autonomous robots in dynamic environments. While prior research has typically focused on either prediction or generation, our a…
Diffusion-Reinforcement Learning Hierarchical Motion Planning in Multi-agent Adversarial Games
Zixuan Wu, Sean Ye, Manisha Natarajan +1
Reinforcement Learning (RL)-based motion planning has recently shown the potential to outperform traditional approaches from autonomous navigation to robot manipulation. In this wo…