Specification-Guided Data Aggregation for Semantically Aware Imitation Learning

Shah, Ameesh; DeCastro, Jonathan; Gideon, John; Yalcinkaya, Beyazit; Rosman, Guy; Seshia, Sanjit A. · 2023 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2303.17010

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Summary

This paper addresses the problem of covariate shift in imitation learning (IL), where inaccuracies in early predictions lead to compounding errors as agents encounter states outside their training distribution. While existing methods like DAgger focus on switching control between the agent and expert to collect data from novel states, they typically treat the environment as fixed. The authors propose a novel approach that leverages configurable simulation environments to aggregate expert data in semantically meaningful ways. The motivation is to create imitation models that accurately reflect the semantics of expert behavior, such as mimicking a novice driver’s specific error patterns, rather than just optimizing for general performance or safety. The method utilizes specification-guided data aggregation. The authors define a set of logical properties relevant to the domain (e.g., whether a collision occurred or a speed limit was exceeded) to create formal specifications. These specifications partition the space of possible environments and agent trajectories into semantically distinct regions based on truth value assignments. The algorithm identifies regions where the learned imitation policy behaves most differently from the expert. It then uses a controllable environment sampler to generate scenarios that satisfy these specific specifications, allowing for targeted collection of expert data in those semantically critical regions. This process ensures that the training data covers high-value, unlikely events that are crucial for understanding the expert’s behavior semantics. The approach was instantiated and evaluated in the CARLA driving simulator. The experiments compared the proposed specification-guided aggregation method against other environment sampling techniques. The results demonstrated that models trained using this method were more accurate in imitating the expert’s behavior semantics than those trained with alternative sampling methods. By prioritizing environments that induce specific logical outcomes, the method effectively broadened the understanding of the expert’s behavior, leading to improved imitation performance. The significance of this work lies in its ability to improve imitation learning by explicitly incorporating semantic awareness into the data aggregation process. By treating the environment as a controllable part of the IL loop and using formal specifications to guide sampling, the method addresses covariate shift in a more targeted manner than traditional approaches. This contributes to the field by showing how formal methods and simulation can be leveraged not just for evaluation, but for actively improving the quality and semantic fidelity of learned policies in safety-critical domains like autonomous driving.

Key finding

Specification-guided data aggregation produces imitation learning models that are more accurate in replicating expert behavior semantics than models trained with other environment sampling methods.

Methodology

simulation_modeling

Provenance

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Topics

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• situational awareness