RISE: Robust Imitation through Stochastic Encodings

Mumuksh Tayal, Manan Tayal, Ravi Prakash

Keywords: Imitation Learning, Safe Control

TL;DR: We propose an imitation-learning framework that encodes noisy measurable environment parameters to yield offline-trained policies that are more robust to perceptual noise and environment uncertainty.

Abstract: Ensuring safety in robotic systems remains a fundamental challenge, especially when deploying offline policy-learning methods such as imitation learning in dynamic environments. Traditional behavior cloning (BC) often fails to generalize when deployed without fine-tuning because it does not account for disturbances in observations that arises in real-world, changing environments. To address this limitation, we propose RISE (Robust Imitation through Stochastic Encodings), a novel imitation-learning framework that explicitly addresses erroneous measurements of environment parameters into policy learning via a variational latent representation. Our framework encodes parameters such as obstacle state, orientation, and velocity into a smooth variational latent space to improve test time generalization. This enables an offline-trained policy to produce actions that are more robust to perceptual noise and environment uncertainty. We validate our approach on two robotic platforms, an autonomous ground vehicle and a Franka Emika Panda manipulator and demonstrate improved safety robustness while maintaining goal-reaching performance compared to baseline methods.

Submission Number: 21