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- This work is part of a broader research thread around TODO, which allow us to TODO. TODO: e.g. For a survey of the field of learned certificates, see this paper. -
- Other work on TODO from our lab include: -
+ 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. +
@article{TODO, + - - + +