1 citations · 1 across the 7 of their papers we have counts for
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Visual Sim-to-Real Learning for Robotic Insertion under Geometric Variations: Application to Rebar Installation
Tao Sun, Beining Han, Patrick Yin +5
Rebar insertion is among the most repetitive and physically demanding tasks on construction sites, and a contact-rich problem at 1.4 mm clearance. The parts, however, vary at two l…
GraspGen-X: Cross-Embodiment 6-DOF Diffusion-based Grasping
Beining Han, Yu-Wei Chao, Erwin Coumans +5
We study cross-embodiment 6-DOF robot grasping. Unlike prior works, we require the model not only to generalize to novel objects / scenes but also to novel gripper morphologies and…
AnyBody: A Benchmark Suite for Cross-Embodiment Manipulation
Meenal Parakh, Alexandre Kirchmeyer, Beining Han +1
Generalizing control policies to novel embodiments remains a fundamental challenge in enabling scalable and transferable learning in robotics. While prior works have explored this…
Procedural Generation of Articulated Simulation-Ready Assets
Abhishek Joshi, Beining Han, Jack Nugent +12
We introduce Infinigen-Articulated, a toolkit for generating realistic, procedurally generated articulated assets for robotics simulation. We include procedural generators for 18 c…
Zero-shot Sim2Real Transfer for Magnet-Based Tactile Sensor on Insertion Tasks
Beining Han, Abhishek Joshi, Jia Deng
Tactile sensing is an important sensing modality for robot manipulation. Among different types of tactile sensors, magnet-based sensors, like u-skin, balance well between high dura…
FetchBench: A Simulation Benchmark for Robot Fetching
Beining Han, Meenal Parakh, Derek Geng +3
Fetching, which includes approaching, grasping, and retrieving, is a critical challenge for robot manipulation tasks. Existing methods primarily focus on table-top scenarios, which…