2 citations · 9 across the 32 of their papers we have counts for
7 papers · 1 filter
The Effects of Robot Motion on Comfort Dynamics of Novice Users in Close-Proximity Human-Robot Interaction
Pierce Howell, Jack Kolb, Yifan Liu +1
Effective and fluent close-proximity human-robot interaction requires understanding how humans get habituated to robots and how robot motion affects human comfort. While prior work…
MARBLER: An Open Platform for Standardized Evaluation of Multi-Robot Reinforcement Learning Algorithms
Reza Torbati, Shubham Lohiya, Shivika Singh +2
Multi-Agent Reinforcement Learning (MARL) has enjoyed significant recent progress thanks, in part, to the integration of deep learning techniques for modeling interactions in compl…
Concurrent Constrained Optimization of Unknown Rewards for Multi-Robot Task Allocation
Sukriti Singh, Anusha Srikanthan, Vivek Mallampati +1
Task allocation can enable effective coordination of multi-robot teams to accomplish tasks that are intractable for individual robots. However, existing approaches to task allocati…
Fast Anticipatory Motion Planning for Close-Proximity Human-Robot Interaction
Sam Scheele, Pierce Howell, Harish Ravichandar
Effective close-proximity human-robot interaction (CP-HRI) requires robots to be able to both efficiently perform tasks as well as adapt to human behavior and preferences. However,…
On the Utility of Koopman Operator Theory in Learning Dexterous Manipulation Skills
Yunhai Han, Mandy Xie, Ye Zhao +1
Despite impressive dexterous manipulation capabilities enabled by learning-based approaches, we are yet to witness widespread adoption beyond well-resourced laboratories. This is l…
Inferring Implicit Trait Preferences for Task Allocation in Heterogeneous Teams
Vivek Mallampati, Harish Ravichandar
Task allocation in heterogeneous multi-agent teams often requires reasoning about multi-dimensional agent traits (i.e., capabilities) and the demands placed on them by tasks. Howev…
Constrained Reinforcement Learning for Dexterous Manipulation
Abhineet Jain, Jack Kolb, Harish Ravichandar
Existing learning approaches to dexterous manipulation use demonstrations or interactions with the environment to train black-box neural networks that provide little control over h…