3 citations · 5 across the 6 of their papers we have counts for
7 papers
Group-Invariant Unsupervised Skill Discovery: Symmetry-aware Skill Representations for Generalizable Behavior
Junwoo Chang, Joseph Park, Roberto Horowitz +2
Unsupervised skill discovery aims to acquire behavior primitives that improve exploration and accelerate downstream task learning. However, existing approaches often ignore the geo…
Symmetry-Aware Steering of Equivariant Diffusion Policies: Benefits and Limits
Minwoo Park, Junwoo Chang, Jongeun Choi +1
Equivariant diffusion policies (EDPs) combine the generative expressivity of diffusion models with the strong generalization and sample efficiency afforded by geometric symmetries.…
Multi-Robot Motion Planning from Vision and Language using Heat-Inspired Diffusion
Jebeom Chae, Junwoo Chang, Seungho Yeom +2
Diffusion models have recently emerged as powerful tools for robot motion planning by capturing the multi-modal distribution of feasible trajectories. However, their extension to m…
Partially Equivariant Reinforcement Learning in Symmetry-Breaking Environments
Junwoo Chang, Minwoo Park, Joohwan Seo +3
Group symmetries provide a powerful inductive bias for reinforcement learning (RL), enabling efficient generalization across symmetric states and actions via group-invariant Markov…
SE(3)-Equivariant Robot Learning and Control: A Tutorial Survey
Joohwan Seo, Soochul Yoo, Junwoo Chang +6
Recent advances in deep learning and Transformers have driven major breakthroughs in robotics by employing techniques such as imitation learning, reinforcement learning, and LLM-ba…
Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning
Junwoo Chang, Hyunwoo Ryu, Jiwoo Kim +5
Diffusion models have risen as a powerful tool in robotics due to their flexibility and multi-modality. While some of these methods effectively address complex problems, they often…
Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation
Hyunwoo Ryu, Jiwoo Kim, Hyunseok An +7
Diffusion generative modeling has become a promising approach for learning robotic manipulation tasks from stochastic human demonstrations. In this paper, we present Diffusion-EDFs…