1 citations · 1 across the 13 of their papers we have counts for
19 papers
Workhorse: Learning Robust Whole-Body Humanoid Loco-Manipulation from Human Data
Songbo Hu, Qiayuan Liao, Yufeng Chi +4
Humanoid robots still struggle to plan contact-rich whole-body manipulation from egocentric RGB and proprioception. Workhorse learns such manipulation from robot-free human demonst…
QF3: Fast Flow RL with Filtered Q-Gradients
Chung Min Kim, Brent Yi, David McAllister +7
Flow policies have become a standard policy class for learning robot behaviors from demonstrations, but reinforcement learning is still critical for improving pre-trained flow poli…
Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents
Yen-Jen Wang, Haozhe Jiang, Shuying Deng +7
Building reliable robot capabilities across diverse tasks requires substantial human effort to develop and maintain skills, design rewards, and integrate perception with control. W…
Counterfactual Video Generation Enables Scalable Humanoid Loco-Manipulation
Zihan Wang, Zhen Wu, Pieter Abbeel +6
Teaching humanoids loco-manipulation skills, such as carrying diverse objects, via visual imitation is a promising path toward generalist robots. However, collecting diverse, high-…
Humanoid Safe Stop via Learned Stoppability Value
Junfeng Long, Pieter Abbeel, Koushil Sreenath +3
Humanoid robots responding to emergency stop commands typically execute a fixed maneuver, without reasoning about whether a safe stop is actually feasible from the current state. W…
VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed Scenes
Yen-Jen Wang, Jiaman Li, Sirui Chen +9
Perception-based humanoid loco-manipulation requires connecting egocentric observations and task instructions to whole-body motion. Learning this mapping requires synchronized egoc…
LadderMan: Learning Humanoid Perceptive Ladder Climbing
Siheng Zhao, Yuanhang Zhang, Ziqi Lu +6
Humanoid robots hold great promise for operating in human-centered environments, yet ladder climbing remains one of the most challenging tasks due to sparse footholds and handholds…
mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning
Kevin Zakka, Qiayuan Liao, Brent Yi +3
We present mjlab, a lightweight, open-source framework for robot learning that combines GPU-accelerated simulation with composable environments and minimal setup friction. mjlab ad…
Flow Policy Gradients for Robot Control
Brent Yi, Hongsuk Choi, Himanshu Gaurav Singh +9
Likelihood-based policy gradient methods are the dominant approach for training robot control policies from rewards. These methods rely on differentiable action likelihoods, which…
Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching
Zhen Wu, Xiaoyu Huang, Lujie Yang +8
While recent advances in humanoid locomotion have achieved stable walking on varied terrains, capturing the agility and adaptivity of highly dynamic human motions remains an open c…
RPL: Learning Robust Humanoid Perceptive Locomotion on Challenging Terrains
Yuanhang Zhang, Younggyo Seo, Juyue Chen +7
Humanoid perceptive locomotion has made significant progress and shows great promise, yet achieving robust multi-directional locomotion on complex terrains remains underexplored. T…
Learning Sim-to-Real Humanoid Locomotion in 15 Minutes
Younggyo Seo, Carmelo Sferrazza, Juyue Chen +3
Massively parallel simulation has reduced reinforcement learning (RL) training time for robots from days to minutes. However, achieving fast and reliable sim-to-real RL for humanoi…
SOMBRL: Scalable and Optimistic Model-Based RL
Bhavya Sukhija, Lenart Treven, Carmelo Sferrazza +3
We address the challenge of efficient exploration in model-based reinforcement learning (MBRL), where the system dynamics are unknown and the RL agent must learn directly from onli…
TWIST2: Scalable, Portable, and Holistic Humanoid Data Collection System
Yanjie Ze, Siheng Zhao, Weizhuo Wang +6
Large-scale data has driven breakthroughs in robotics, from language models to vision-language-action models in bimanual manipulation. However, humanoid robotics lacks equally effe…
GaussGym: An open-source real-to-sim framework for learning locomotion from pixels
Alejandro Escontrela, Justin Kerr, Arthur Allshire +4
We present a novel approach for photorealistic robot simulation that integrates 3D Gaussian Splatting as a drop-in renderer within vectorized physics simulators such as IsaacGym. T…
ResMimic: From General Motion Tracking to Humanoid Whole-body Loco-Manipulation via Residual Learning
Siheng Zhao, Yanjie Ze, Yue Wang +4
Humanoid whole-body loco-manipulation promises transformative capabilities for daily service and warehouse tasks. While recent advances in general motion tracking (GMT) have enable…
Residual Off-Policy RL for Finetuning Behavior Cloning Policies
Lars Ankile, Zhenyu Jiang, Rocky Duan +3
Recent advances in behavior cloning (BC) have enabled impressive visuomotor control policies. However, these approaches are limited by the quality of human demonstrations, the manu…
OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction
Lujie Yang, Xiaoyu Huang, Zhen Wu +6
A dominant paradigm for teaching humanoid robots complex skills is to retarget human motions as kinematic references to train reinforcement learning (RL) policies. However, existin…
SARM: Stage-Aware Reward Modeling for Long Horizon Robot Manipulation
Qianzhong Chen, Justin Yu, Mac Schwager +3
Large-scale robot learning has made progress on complex manipulation tasks, yet long horizon, contact rich problems, especially those involving deformable objects, remain challengi…