1 citations · 1 across the 10 of their papers we have counts for
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T2-z: Test-Time Optimization of Environment Latents for Robotic World Models
Chenyuan Zhou, Yining Hong, Huang Huang +9
Predicting how the world responds to an action requires more than what can be seen: robots must account for environmental factors that are neither directly observable nor explicitl…
Force-Modulated Dexterity: Unified Compliance for Contact-Rich Manipulation
Ying Yuan, David Held, Jitendra Malik +1
Teleoperation is widely used to collect high-quality demonstrations for robot learning. Dexterous manipulation, however, requires coordinated control of motion and contact forces.…
Scouting the Dynamics Gap: Test-Time Policy Adaptation via Action-Outcome Feedback
Yishu Li, Liyuan Geng, Xinyi Mao +2
While pretrained robotic policies exhibit impressive capabilities in controlled environments, unobserved physical properties and dynamics require these policies to rapidly adapt du…
H2RBench: A Real-to-Sim Benchmark for Evaluating Human-to-Robot Transfer
Chuyang Xiao, Haotian Zhan, Sriram Krishna +4
Learning robot manipulation policies from human video demonstrations constitutes a promising avenue for scalable robot learning. However, comparing different human-to-robot (H2R) t…
JAMB: Joint Action-Motion Diffusion for Bimanual Manipulation
Chuyang Xiao, Peilin Meng, David Held
Coordinated bimanual manipulation is challenging because the motion of either arm can alter the shared 3D scene and thereby affect the other arm. Yet most diffusion policies genera…
From Grasps to Dexterity: Large-Scale Grasp Pretraining for Dexterous Manipulation
Ying Yuan, Xinyu Liu, Sriram Krishna +1
Large-scale dexterous grasp datasets encode rich priors over hand-object interaction, but their use has largely been confined to grasp generation and pick-and-place manipulation. W…
GHOST: Hierarchical Sub-Goal Policies for Generalizing Robot Manipulation
Sriram Krishna, Ben Eisner, Haotian Zhan +5
We present GHOST, a framework for learning visuomotor manipulation policies that generalize beyond the training distribution. GHOST factorizes control into (i) a high-level policy…
RoboWits: Unexpected Challenges for Robotic Creative Problem Solving
Chunru Lin, Hongxin Zhang, Fenghao Yu +5
The ability to reason, adapt, and creatively solve problems under unexpected challenges is essential for robots operating in real-world environments. However, current robotic bench…