GenOSIL: Generalized Optimal and Safe Robot Control using Parameter-Conditioned Imitation Learning

2025-03-15

AI Summary

To address insufficient safety assurance and generalization in imitation learning under dynamic environments, this paper proposes a safety-aware imitation learning framework based on structured latent-variable modeling. Methodologically, it introduces a variational autoencoder (VAE) to explicitly encode measurable environmental safety parameters—such as obstacle positions, velocities, and geometries—into a structured latent space, enabling the policy to reason about the underlying safety logic of expert demonstrations rather than merely replicating trajectories. A parameter-conditioned policy network and a safety-constraint-aware architecture are further designed to shift the paradigm from behavioral cloning to safety-oriented reasoning. Extensive evaluations on autonomous driving and Franka robotic arm platforms—spanning both simulation and real-world deployment—demonstrate substantial improvements in task success rate and collision avoidance capability. The proposed approach consistently outperforms baseline methods including Behavior Cloning (BC) and Generative Adversarial Imitation Learning (GAIL).

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Ensuring safe and generalizable control remains a fundamental challenge in robotics, particularly when deploying imitation learning in dynamic environments. Traditional behavior cloning (BC) struggles to generalize beyond its training distribution, as it lacks an understanding of the safety critical reasoning behind expert demonstrations. To address this limitation, we propose GenOSIL, a novel imitation learning framework that explicitly incorporates environment parameters into policy learning via a structured latent representation. Unlike conventional methods that treat the environment as a black box, GenOSIL employs a variational autoencoder (VAE) to encode measurable safety parameters such as obstacle position, velocity, and geometry into a latent space that captures intrinsic correlations between expert behavior and environmental constraints. This enables the policy to infer the rationale behind expert trajectories rather than merely replicating them. We validate our approach on two robotic platforms an autonomous ground vehicle and a Franka Emika Panda manipulator demonstrating superior safety and goal reaching performance compared to baseline methods. The simulation and hardware videos can be viewed on the project webpage: https://mumukshtayal.github.io/GenOSIL/.

Problem

• Ensures safe and generalizable robot control in dynamic environments.
• Addresses limitations of traditional behavior cloning in imitation learning.
• Incorporates environment parameters into policy learning for improved safety.

Innovation

• Incorporates environment parameters via structured latent representation
• Uses VAE to encode safety parameters into latent space
• Enables policy to infer expert trajectory rationale