RADIUM: Predicting and Repairing End-to-End Robot Failures using Gradient-Accelerated Sampling
arxiv(2024)
摘要
Before autonomous systems can be deployed in safety-critical applications, we
must be able to understand and verify the safety of these systems. For cases
where the risk or cost of real-world testing is prohibitive, we propose a
simulation-based framework for a) predicting ways in which an autonomous system
is likely to fail and b) automatically adjusting the system's design and
control policy to preemptively mitigate those failures. Existing tools for
failure prediction struggle to search over high-dimensional environmental
parameters, cannot efficiently handle end-to-end testing for systems with
vision in the loop, and provide little guidance on how to mitigate failures
once they are discovered. We approach this problem through the lens of
approximate Bayesian inference and use differentiable simulation and rendering
for efficient failure case prediction and repair. For cases where a
differentiable simulator is not available, we provide a gradient-free version
of our algorithm, and we include a theoretical and empirical evaluation of the
trade-offs between gradient-based and gradient-free methods. We apply our
approach on a range of robotics and control problems, including optimizing
search patterns for robot swarms, UAV formation control, and robust network
control. Compared to optimization-based falsification methods, our method
predicts a more diverse, representative set of failure modes, and we find that
our use of differentiable simulation yields solutions that have up to 10x lower
cost and requires up to 2x fewer iterations to converge relative to
gradient-free techniques. In hardware experiments, we find that repairing
control policies using our method leads to a 5x robustness improvement.
Accompanying code and video can be found at https://mit-realm.github.io/radium/
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