1 citations · 1 across the 10 of their papers we have counts for
14 papers
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…
Planning from Point Clouds over Continuous Actions for Multi-object Rearrangement
Kallol Saha, Amber Li, Angela Rodriguez-Izquierdo +4
Long-horizon planning for robot manipulation is a challenging problem that requires reasoning about the effects of a sequence of actions on a physical 3D scene. While traditional t…
Learn from What We HAVE: History-Aware VErifier that Reasons about Past Interactions Online
Yishu Li, Xinyi Mao, Ying Yuan +3
We introduce a novel History-Aware VErifier (HAVE) to disambiguate uncertain scenarios online by leveraging past interactions. Robots frequently encounter visually ambiguous object…
Non-rigid Relative Placement through 3D Dense Diffusion
Eric Cai, Octavian Donca, Ben Eisner +1
The task of "relative placement" is to predict the placement of one object in relation to another, e.g. placing a mug onto a mug rack. Through explicit object-centric geometric rea…
FlowBotHD: History-Aware Diffuser Handling Ambiguities in Articulated Objects Manipulation
Yishu Li, Wen Hui Leng, Yiming Fang +2
We introduce a novel approach for manipulating articulated objects which are visually ambiguous, such doors which are symmetric or which are heavily occluded. These ambiguities can…
Deep SE(3)-Equivariant Geometric Reasoning for Precise Placement Tasks
Ben Eisner, Yi Yang, Todor Davchev +3
Many robot manipulation tasks can be framed as geometric reasoning tasks, where an agent must be able to precisely manipulate an object into a position that satisfies the task from…
On Time-Indexing as Inductive Bias in Deep RL for Sequential Manipulation Tasks
M. Nomaan Qureshi, Ben Eisner, David Held
While solving complex manipulation tasks, manipulation policies often need to learn a set of diverse skills to accomplish these tasks. The set of skills is often quite multimodal -…