3 papers
cs.RO2026
AnyTask: an Automated Task and Data Generation Framework for Advancing Sim-to-Real Policy Learning
Ran Gong, Xiaohan Zhang, Jinghuan Shang +11
Generalist robot learning remains constrained by data: large-scale, diverse, and high-quality interaction data are expensive to collect in the real world. While simulation has beco…
cs.LG2025
Data-Efficient Multitask DAgger
Haotian Fu, Ran Gong, Xiaohan Zhang +3
Generalist robot policies that can perform many tasks typically require extensive expert data or simulations for training. In this work, we propose a novel Data-Efficient multitask…
cs.RO2025
ParticleFormer: A 3D Point Cloud World Model for Multi-Object, Multi-Material Robotic Manipulation
Suning Huang, Qianzhong Chen, Xiaohan Zhang +2
3D world models (i.e., learning-based 3D dynamics models) offer a promising approach to generalizable robotic manipulation by capturing the underlying physics of environment evolut…