9 papers
Scalable Multi-Task Data Generation via Reinforcement Learning for Language-Conditioned Bimanual Dexterous Manipulation
Zechu Li, Yufeng Jin, Puze Liu +2
A key bottleneck in training generalist policies for bimanual dexterous manipulation is the lack of large-scale, high-quality datasets. Synthetic data generation in simulation prov…
Bimanual Robot Manipulation via Multi-Agent In-Context Learning
Alessio Palma, Indro Spinelli, Vignesh Prasad +4
Language Models (LLMs) have emerged as powerful reasoning engines for embodied control. In particular, In-Context Learning (ICL) enables off-the-shelf, text-only LLMs to predict ro…
Robot-DIFT: Correspondence-Sensitive Diffusion Features for Contact-Rich Robot Manipulation
Yu Deng, Yufeng Jin, Xiaogang Jia +3
Robot manipulation often fails in the final millimeters: a policy may recognize the right object yet miss the pose offsets, boundaries, or pre-contact alignments needed for action.…
HARBOR: A Harness Framework for Agentic Robot Reinforcement Learning
Zechu Li, Yufeng Jin, Xiaoyang Liu +4
Reinforcement learning (RL) has become a powerful paradigm for robot learning, particularly in sim-to-real settings, but its broader adoption remains limited by the engineering pip…
Stein Variational Ergodic Surface Coverage with SE(3) Constraints
Jiayun Li, Yufeng Jin, Sangli Teng +2
Surface manipulation tasks require robots to generate trajectories that comprehensively cover complex 3D surfaces while maintaining precise end-effector poses. Existing ergodic tra…
Nautilus: From One Prompt to Plug-and-Play Robot Learning
Yufeng Jin, Jianfei Guo, Xiaogang Jia +8
Robot learning research is fragmented across policy families, benchmark suites, and real robots; each implementation is entangled with the others in a complex combination matrix, m…