580 citations · 1.9k across the 92 of their papers we have counts for
16 papers · 1 filter
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…
EyeRobot 2.0: Active Gaze for Precise Manipulation without Wrist Cameras
Kush Hari, Justin Kerr, Nidhya Shivakumar +7
Inspired by human vision, we introduce a framework using active gaze to enable fine-grained bimanual manipulation with only a single stereo camera. EyeRobot 2.0 physically attends…
World Motion Models: Flexible Sequence Modeling of SE(3) Trajectories
Jiahui Lei, Qianqian Wang, Trevor Darrell +1
Equipping artificial agents with spatial intelligence requires a comprehensive generative prior over the dynamic 3D world. We propose World Motion Models (WMMs) that capture "what…
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-…
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…
Scalable Behavior Cloning with Open Data, Training, and Evaluation
Arthur Allshire, Himanshu Gaurav Singh, Ritvik Singh +15
We introduce ABC, a fully open-source stack for manipulation with behavior cloning. At its core is ABC-130K: the largest open-source teleoperation dataset to date, featuring 3,500…
Playful Agentic Robot Learning
Junyi Zhang, Jiaxin Ge, Hanjun Yoo +17
Current agentic robot systems can write executable Code-as-Policy programs, observe feedback, and revise behavior across multiple attempts, but they remain largely task-driven: reu…
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…
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…
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…
Eye, Robot: Learning to Look to Act with a BC-RL Perception-Action Loop
Justin Kerr, Kush Hari, Ethan Weber +5
Humans do not passively observe the visual world -- we actively look in order to act. Motivated by this principle, we introduce EyeRobot, a robotic system with gaze behavior that e…
PyRoki: A Modular Toolkit for Robot Kinematic Optimization
Chung Min Kim, Brent Yi, Hongsuk Choi +3
Robot motion can have many goals. Depending on the task, we might optimize for pose error, speed, collision, or similarity to a human demonstration. Motivated by this, we present P…
Visual Imitation Enables Contextual Humanoid Control
Arthur Allshire, Hongsuk Choi, Junyi Zhang +7
How can we teach humanoids to climb staircases and sit on chairs using the surrounding environment context? Arguably, the simplest way is to just show them-casually capture a human…
Robot See Robot Do: Imitating Articulated Object Manipulation with Monocular 4D Reconstruction
Justin Kerr, Chung Min Kim, Mingxuan Wu +4
Humans can learn to manipulate new objects by simply watching others; providing robots with the ability to learn from such demonstrations would enable a natural interface specifyin…
Language Embedded Radiance Fields for Zero-Shot Task-Oriented Grasping
Adam Rashid, Satvik Sharma, Chung Min Kim +4
Grasping objects by a specific part is often crucial for safety and for executing downstream tasks. Yet, learning-based grasp planners lack this behavior unless they are trained on…