RISE: Robust Imitation through Stochastic Encoding

• URL: http://arxiv.org/abs/2503.12243v2
• Date: Sat, 27 Sep 2025 12:47:07 GMT
• Title: RISE: Robust Imitation through Stochastic Encoding
• Authors: Mumuksh Tayal, Manan Tayal, Ravi Prakash,
• Abstract summary: We propose a novel imitation-learning framework that explicitly addresses erroneous measurements of environment parameters into policy learning.<n>Our framework encodes parameters such as obstacle state, orientation, and velocity into a latent space to improve test time.<n>We validate our approach on two robotic platforms and demonstrate improved safety while maintaining goal-reaching performance compared to baseline methods.
• Score: 0.764671395172401
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 as 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.

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