8 citations · 10 across the 7 of their papers we have counts for
4 papers · 1 filter
Generalizable Hierarchical Skill Learning via Object-Centric Representation
Haibo Zhao, Yu Qi, Boce Hu +9
We present Generalizable Hierarchical Skill Learning (GSL), a novel framework for hierarchical policy learning that significantly improves policy generalization and sample efficien…
Hierarchical Equivariant Policy via Frame Transfer
Haibo Zhao, Dian Wang, Yizhe Zhu +6
Recent advances in hierarchical policy learning highlight the advantages of decomposing systems into high-level and low-level agents, enabling efficient long-horizon reasoning and…
Equivariant Action Sampling for Reinforcement Learning and Planning
Linfeng Zhao, Owen Howell, Xupeng Zhu +4
Reinforcement learning (RL) algorithms for continuous control tasks require accurate sampling-based action selection. Many tasks, such as robotic manipulation, contain inherent pro…
Fourier Transporter: Bi-Equivariant Robotic Manipulation in 3D
Haojie Huang, Owen Howell, Dian Wang +3
Many complex robotic manipulation tasks can be decomposed as a sequence of pick and place actions. Training a robotic agent to learn this sequence over many different starting cond…