Learned Risk Metric Maps for Kinodynamic Systems

• URL: http://arxiv.org/abs/2302.14803v1
• Date: Tue, 28 Feb 2023 17:51:43 GMT
• Title: Learned Risk Metric Maps for Kinodynamic Systems
• Authors: Ross Allen, Wei Xiao, Daniela Rus
• Abstract summary: We present Learned Risk Metric Maps for real-time estimation of coherent risk metrics of high dimensional dynamical systems. LRMM models are simple to design and train, requiring only procedural generation of obstacle sets, state and control sampling, and supervised training of a function approximator.
• Score: 54.49871675894546
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Learned Risk Metric Maps (LRMM) for real-time estimation of coherent risk metrics of high dimensional dynamical systems operating in unstructured, partially observed environments. LRMM models are simple to design and train -- requiring only procedural generation of obstacle sets, state and control sampling, and supervised training of a function approximator -- which makes them broadly applicable to arbitrary system dynamics and obstacle sets. In a parallel autonomy setting, we demonstrate the model's ability to rapidly infer collision probabilities of a fast-moving car-like robot driving recklessly in an obstructed environment; allowing the LRMM agent to intervene, take control of the vehicle, and avoid collisions. In this time-critical scenario, we show that LRMMs can evaluate risk metrics 20-100x times faster than alternative safety algorithms based on control barrier functions (CBFs) and Hamilton-Jacobi reachability (HJ-reach), leading to 5-15\% fewer obstacle collisions by the LRMM agent than CBFs and HJ-reach. This performance improvement comes in spite of the fact that the LRMM model only has access to local/partial observation of obstacles, whereas the CBF and HJ-reach agents are granted privileged/global information. We also show that our model can be equally well trained on a 12-dimensional quadrotor system operating in an obstructed indoor environment. The LRMM codebase is provided at https://github.com/mit-drl/pyrmm.

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