23 citations · 83 across the 14 of their papers we have counts for
26 papers · 1 filter
HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers
Yu-Wei Chao, Chris Paxton, Yu Xiang +6
We introduce a new simulation benchmark "HandoverSim" for human-to-robot object handovers. To simulate the giver's motion, we leverage a recent motion capture dataset of hand grasp…
Correcting Robot Plans with Natural Language Feedback
Pratyusha Sharma, Balakumar Sundaralingam, Valts Blukis +5
When humans design cost or goal specifications for robots, they often produce specifications that are ambiguous, underspecified, or beyond planners' ability to solve. In these case…
Model Predictive Control for Fluid Human-to-Robot Handovers
Wei Yang, Balakumar Sundaralingam, Chris Paxton +4
Human-robot handover is a fundamental yet challenging task in human-robot interaction and collaboration. Recently, remarkable progressions have been made in human-to-robot handover…
IFOR: Iterative Flow Minimization for Robotic Object Rearrangement
Ankit Goyal, Arsalan Mousavian, Chris Paxton +4
Accurate object rearrangement from vision is a crucial problem for a wide variety of real-world robotics applications in unstructured environments. We propose IFOR, Iterative Flow…
StructFormer: Learning Spatial Structure for Language-Guided Semantic Rearrangement of Novel Objects
Weiyu Liu, Chris Paxton, Tucker Hermans +1
Geometric organization of objects into semantically meaningful arrangements pervades the built world. As such, assistive robots operating in warehouses, offices, and homes would gr…
Predicting Stable Configurations for Semantic Placement of Novel Objects
Chris Paxton, Chris Xie, Tucker Hermans +1
Human environments contain numerous objects configured in a variety of arrangements. Our goal is to enable robots to repose previously unseen objects according to learned semantic…