Explaining the Decisions of Deep Policy Networks for Robotic Manipulations

no code implementations • 30 Oct 2023 • Seongun Kim, Jaesik Choi

In this paper, we present an explicit analysis of deep policy models through input attribution methods to explain how and to what extent each input feature affects the decisions of the robot policy models.

Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills

1 code implementation • 30 Oct 2023 • Seongun Kim, Kyowoon Lee, Jaesik Choi

We validate the effectiveness of our approach on complex navigation and robotic manipulation tasks in terms of sample efficiency and state coverage speed.

reinforcement-learning Reinforcement Learning (RL) +1

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

1 code implementation • NeurIPS 2023 • Kyowoon Lee, Seongun Kim, Jaesik Choi

We also illustrate that our approach presents explainability by presenting the attribution maps of the gap predictor and highlighting error-prone transitions, allowing for a deeper understanding of the generated plans.