1 citations · 1 across the 4 of their papers we have counts for
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FINGR: Learning Dexterous Hand Control for Real-World Rubik's Cube Solving
Yutong Liang, Quanquan Peng, Matthew Kim +1
Manipulating a Rubik's Cube with a single dexterous hand is a challenging test of sustained, contact-rich control: the hand must execute successive layer turns while keeping the cu…
REFINE-DP: Diffusion Policy Fine-tuning for Humanoid Loco-manipulation via Reinforcement Learning
Zhaoyuan Gu, Yipu Chen, Zimeng Chai +11
Humanoid loco-manipulation requires coordinated task-space motion planning with stable loco-manipulation command tracking under complex robot-environment dynamics and long-horizon…
How Well Do Latent World Models Understand Partially Observable Safety Constraints?
Matthew Kim, Kensuke Nakamura, Andrea Bajcsy
Latent world models are a promising approach for learning state representations and dynamics directly from high-dimensional observations, enabling robot control in hard-to-model se…
DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation
Hao-Shu Fang, Branden Romero, Yichen Xie +9
We introduce perioperation, a paradigm for robotic data collection that sensorizes and records human manipulation while maximizing the transferability of the data to real robots. W…
Reachability Barrier Networks: Learning Hamilton-Jacobi Solutions for Smooth and Flexible Control Barrier Functions
Matthew Kim, William Sharpless, Hyun Joe Jeong +3
Recent developments in autonomous driving and robotics underscore the necessity of safety-critical controllers. Control barrier functions (CBFs) are a popular method for appending…