103 citations · 108 across the 22 of their papers we have counts for
23 papers
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…
GR-RL: Going Dexterous and Precise for Long-Horizon Robotic Manipulation
Yunfei Li, Xiao Ma, Jiafeng Xu +18
We present GR-RL, a robotic learning framework that turns a generalist vision-language-action (VLA) policy into a highly capable specialist for long-horizon dexterous manipulation.…
TRACE: Textual Reasoning for Affordance Coordinate Extraction
Sangyun Park, Jin Kim, Yuchen Cui +1
Vision-Language Models (VLMs) struggle to translate high-level instructions into the precise spatial affordances required for robotic manipulation. While visual Chain-of-Thought (C…
Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models
Huihan Liu, Rutav Shah, Shuijing Liu +6
Assistive teleoperation, where control is shared between a human and a robot, enables efficient and intuitive human-robot collaboration in diverse and unstructured environments. A…
Towards Embodiment Scaling Laws in Robot Locomotion
Bo Ai, Liu Dai, Nico Bohlinger +7
Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…
How to Train Your Robots? The Impact of Demonstration Modality on Imitation Learning
Haozhuo Li, Yuchen Cui, Dorsa Sadigh
Imitation learning is a promising approach for learning robot policies with user-provided data. The way demonstrations are provided, i.e., demonstration modality, influences the qu…
Shared Autonomy for Proximal Teaching
Megha Srivastava, Reihaneh Iranmanesh, Yuchen Cui +6
Motor skill learning often requires experienced professionals who can provide personalized instruction. Unfortunately, the availability of high-quality training can be limited for…
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…
Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theory
Zhi Zhang, Chris Chow, Yasi Zhang +7
Lifelong reinforcement learning (RL) has been developed as a paradigm for extending single-task RL to more realistic, dynamic settings. In lifelong RL, the "life" of an RL agent is…
FlowRetrieval: Flow-Guided Data Retrieval for Few-Shot Imitation Learning
Li-Heng Lin, Yuchen Cui, Amber Xie +2
Few-shot imitation learning relies on only a small amount of task-specific demonstrations to efficiently adapt a policy for a given downstream tasks. Retrieval-based methods come w…
Distilling and Retrieving Generalizable Knowledge for Robot Manipulation via Language Corrections
Lihan Zha, Yuchen Cui, Li-Heng Lin +5
Today's robot policies exhibit subpar performance when faced with the challenge of generalizing to novel environments. Human corrective feedback is a crucial form of guidance to en…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
Gesture-Informed Robot Assistance via Foundation Models
Li-Heng Lin, Yuchen Cui, Yilun Hao +2
Gestures serve as a fundamental and significant mode of non-verbal communication among humans. Deictic gestures (such as pointing towards an object), in particular, offer valuable…