18 citations · 28 across the 8 of their papers we have counts for
8 papers · 1 filter
ModPack: Extensible Teleoperation Interface for Bimanual Mobile Manipulation
Joshua Citron, Renee Zbizika, Zeyi Liu +1
Existing teleoperation systems are often tailored to specific robot hardware and task domains, limiting their scalability and adaptability. We present ModPack, a modular and extens…
EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World
Ryan Punamiya, Simar Kareer, Zeyi Liu +37
Robot learning increasingly depends on large and diverse data, yet robot data collection remains expensive and difficult to scale. Egocentric human data offer a promising alternati…
Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections
Xiaomeng Xu, Yifan Hou, Chendong Xin +2
We address key challenges in Dataset Aggregation (DAgger) for real-world contact-rich manipulation: how to collect informative human correction data and how to effectively update p…
Adaptive Compliance Policy: Learning Approximate Compliance for Diffusion Guided Control
Yifan Hou, Zeyi Liu, Cheng Chi +5
Compliance plays a crucial role in manipulation, as it balances between the concurrent control of position and force under uncertainties. Yet compliance is often overlooked by toda…
ManiWAV: Learning Robot Manipulation from In-the-Wild Audio-Visual Data
Zeyi Liu, Cheng Chi, Eric Cousineau +3
Audio signals provide rich information for the robot interaction and object properties through contact. This information can surprisingly ease the learning of contact-rich robot ma…
ContactHandover: Contact-Guided Robot-to-Human Object Handover
Zixi Wang, Zeyi Liu, Nicolas Ouporov +1
Robot-to-human object handover is an important step in many human robot collaboration tasks. A successful handover requires the robot to maintain a stable grasp on the object while…
REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction
Zeyi Liu, Arpit Bahety, Shuran Song
The ability to detect and analyze failed executions automatically is crucial for an explainable and robust robotic system. Recently, Large Language Models (LLMs) have demonstrated…
BusyBot: Learning to Interact, Reason, and Plan in a BusyBoard Environment
Zeyi Liu, Zhenjia Xu, Shuran Song
We introduce BusyBoard, a toy-inspired robot learning environment that leverages a diverse set of articulated objects and inter-object functional relations to provide rich visual f…