Heedo Woo — accepted AI-conference papers

2025

D4orm: Multi-Robot Trajectories with Dynamics-aware Diffusion Denoised Deformations

Yuhao Zhang, Keisuke Okumura, Heedo Woo, Ajay Shankar, Amanda Prorok

IROS 2025

This work presents an optimization method for generating kinodynamically feasible and collision-free multi-robot trajectories that exploits an incremental denoising scheme in diffusion models. Our key insight is that high-quality trajectories can be discovered merely by denoising noisy trajectories

Cited by 2 Source ↗Scholar ↗

2025

ReCoDe: Reinforcement Learning-based Dynamic Constraint Design for Multi-Agent Coordination

Michael Amir, Guang Yang, Zhan Gao, Keisuke Okumura, Heedo Woo, Amanda Prorok

CoRL 2025poster

Constraint-based optimization is a cornerstone of robotics, enabling the design of controllers that reliably encode task and safety requirements such as collision avoidance or formation adherence. However, handcrafted constraints can fail in multi-agent settings that demand complex coordination. We…

Cited by 0 Source ↗Scholar ↗