Seongun Kim

Explaining the Decisions of Deep Policy Networks for Robotic Manipulations

2021 · 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Deep policy networks enable robots to learn behaviors to solve various real-world complex tasks in an end-to-end fashion. However, they lack transparency to provide the reasons of actions. Thus, such a black-box model often results …

Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans

2023 · arXiv (Cornell University)

Diffusion-based planning has shown promising results in long-horizon, sparse-reward tasks by training trajectory diffusion models and conditioning the sampled trajectories using auxiliary guidance functions. However, due to their nature as generative models, diffusion models are …