SAFE-GIL: SAFEty Guided Imitation Learning

• URL: http://arxiv.org/abs/2404.05249v1
• Date: Mon, 8 Apr 2024 07:25:25 GMT
• Title: SAFE-GIL: SAFEty Guided Imitation Learning
• Authors: Yusuf Umut Ciftci, Zeyuan Feng, Somil Bansal,
• Abstract summary: Behavior cloning is a popular approach to Imitation Learning, in which a robot observes an expert supervisor and learns a control policy. However, behavior cloning suffers from the "compounding error" problem - the policy errors compound as it deviates from the expert demonstrations and might lead to catastrophic system failures. We propose SAFE-GIL, an off-policy behavior cloning method that guides the expert via adversarial disturbance during data collection.
• Score: 7.979892202477701
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Behavior Cloning is a popular approach to Imitation Learning, in which a robot observes an expert supervisor and learns a control policy. However, behavior cloning suffers from the "compounding error" problem - the policy errors compound as it deviates from the expert demonstrations and might lead to catastrophic system failures, limiting its use in safety-critical applications. On-policy data aggregation methods are able to address this issue at the cost of rolling out and repeated training of the imitation policy, which can be tedious and computationally prohibitive. We propose SAFE-GIL, an off-policy behavior cloning method that guides the expert via adversarial disturbance during data collection. The algorithm abstracts the imitation error as an adversarial disturbance in the system dynamics, injects it during data collection to expose the expert to safety critical states, and collects corrective actions. Our method biases training to more closely replicate expert behavior in safety-critical states and allows more variance in less critical states. We compare our method with several behavior cloning techniques and DAgger on autonomous navigation and autonomous taxiing tasks and show higher task success and safety, especially in low data regimes where the likelihood of error is higher, at a slight drop in the performance.

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