6 papers
Whole-Body Dexterity
Conor Mc Gartoll, An Dang, Pranay Thangeda +5
Current humanoid dexterous manipulation remains largely confined to end-effectors and simple grasps, falling short of humans' ability to engage the entire body by bracing and slidi…
HULK: Learning Whole-Body Forceful Loco-Manipulation for Humanoids
An Dang, Arturo Flores Alvarez, Yu-Ming Chen +5
Humanoid loco-manipulation of large, heavy objects demands forceful interaction across the entire body. However, such payloads shift a humanoid's center of mass and impose sustaine…
LIMBO: Learning and Internalizing Model-Free Barrier Objectives for Agile and Safe Whole-Body Control
Jake Gonzales, Arturo Flores Alvarez, Yu-Ming Chen +3
Safe whole-body control requires coordinating collision avoidance and balance under high-dimensional, nonlinear dynamics--making safety certificates difficult to design and reuse a…
HydroShear: Hydroelastic Shear Simulation for Tactile Sim-to-Real Reinforcement Learning
An Dang, Jayjun Lee, Mustafa Mukadam +4
In this paper, we address the problem of tactile sim-to-real policy transfer for contact-rich tasks. Existing methods primarily focus on vision-based sensors and emphasize image re…
Self-Refining Vision Language Model for Robotic Failure Detection and Reasoning
Carl Qi, Xiaojie Wang, Silong Yong +6
Reasoning about failures is crucial for building reliable and trustworthy robotic systems. Prior approaches either treat failure reasoning as a closed-set classification problem or…
ARMBench: An Object-centric Benchmark Dataset for Robotic Manipulation
Chaitanya Mitash, Fan Wang, Shiyang Lu +4
This paper introduces Amazon Robotic Manipulation Benchmark (ARMBench), a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. Auto…