103 citations · 108 across the 22 of their papers we have counts for
11 papers · 1 filter
FetchMan: Learning Visual Humanoid Loco-Manipulation Policies from Simulated Experiences
Omar Rayyan, Zhi Li, Max Argus +4
Visual loco-manipulation policies that can generalize to novel scenes and objects have long been a goal of robotics research. However, today's data-hungry algorithms make collectin…
Meta-Ctrl: Guaranteed Plan Generation by Decoupling Syntactic and Semantic Constraints
Gwen Yidou-Weng, Edward Sun, Tianyi Ma +5
LLMs generate fluent plans for robots but routinely violate the syntactic and se8mantic constraints they must satisfy to execute, and existing remedies trade formal guarantees agai…
DexDirect: Direct Kinesthetic Arm Guidance for Efficient Dexterous Demonstration Collection
Beom Jun Kim, Shiu-Jen Wang, Jonathan Liu +7
Scalable collection of dexterous manipulation demonstrations remains a major bottleneck for robot learning. High-fidelity interfaces often require costly hardware and extensive set…
Proximal State Nudging: Reducing Skill Atrophy from AI Assistance
Megha Srivastava, Jonathan Ouyang, Eric Zhou +6
Skill atrophy, the gradual decline of human capability under AI assistance, poses a safety risk in shared-control of semi-autonomous systems, where operators may be unable to disti…
Learning from the Best: Smoothness-Driven Metrics for Data Quality in Imitation Learning
Soham Kulkarni, Raayan Dhar, Yuchen Cui
In behavioral cloning (BC), policy performance is fundamentally limited by demonstration data quality. Real-world datasets contain trajectories of varying quality due to operator s…
TeleDex: Accessible Dexterous Teleoperation
Omar Rayyan, Maximilian Gilles, Yuchen Cui
Despite increasing dataset scale and model capacity, robot manipulation policies still struggle to generalize beyond their training distributions. As a result, deploying state-of-t…
DexEXO: A Wearability-First Dexterous Exoskeleton for Operator-Agnostic Demonstration and Learning
Alvin Zhu, Mingzhang Zhu, Beom Jun Kim +10
Scaling dexterous robot learning is constrained by the difficulty of collecting high-quality demonstrations across diverse operators. Existing wearable interfaces often trade comfo…
WHED: A Wearable Hand Exoskeleton for Natural, High-Quality Demonstration Collection
Mingzhang Zhu, Alvin Zhu, Jose Victor S. H. Ramos +9
Scalable learning of dexterous manipulation remains bottlenecked by the difficulty of collecting natural, high-fidelity human demonstrations of multi-finger hands due to occlusion,…
DiSCo: Diffusion Sequence Copilots for Shared Autonomy
Andy Wang, Xu Yan, Brandon McMahan +9
Shared autonomy combines human user and AI copilot actions to control complex systems such as robotic arms. When a task is challenging, requires high dimensional control, or is sub…
MolmoSpaces: A Large-Scale Open Ecosystem for Robot Navigation and Manipulation
Yejin Kim, Wilbert Pumacay, Omar Rayyan +23
Deploying robots at scale demands robustness to the long tail of everyday situations. The countless variations in scene layout, object geometry, and task specifications that charac…
Intent at a Glance: Gaze-Guided Robotic Manipulation via Foundation Models
Tracey Yee Hsin Tay, Xu Yan, Jonathan Ouyang +4
Designing intuitive interfaces for robotic control remains a central challenge in enabling effective human-robot interaction, particularly in assistive care settings. Eye gaze offe…