IROS 2022 Risk Sensitive MPC

Risk-sensitive MPCs with Deep Distributional Inverse RL for Autonomous Driving

K. Lee D. Isele E. Theodorou S. Bae

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2022, K

In robot learning from demonstration (LfD), a visual representation of a cost function inferred from Inverse Reinforcement Learning (IRL) provides an intuitive tool for humans to quickly interpret the underlying objectives of the demonstration. The inferred cost function can be used by controllers, for example, Model Predictive Controllers (MPCs). In this work, we improve the recently developed IRL-MPC framework, by enhancing it in a risk-sensitive formulation to be more applicable for safety-critical applications like autonomous driving. Our risk-sensitive MPCs together with the distributional costmap demonstrate lower collision rates in the CARLA simulator for autonomous driving tasks compared to other learning-based baseline methods.

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