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Creators/Authors contains: "Sheckells, Matthew"

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  1. This work develops a technique for using robot motion trajectories to create a high quality stochastic dynamics model that is then leveraged in simulation to train control policies with associated performance guarantees. We demonstrate the idea by collecting dynamics data from a 1/5 scale agile ground vehicle, fitting a stochastic dynamics model, and training a policy in simulation to drive around an oval track at up to 6.5 m/s while avoiding obstacles. We show that the control policy can be transferred back to the real vehicle with little loss in predicted performance. We compare this to an approach that uses a simple analytic car model to train a policy in simulation and show that using a model with stochasticity learned from data leads to higher performance in terms of trajectory tracking accuracy and collision probability. Furthermore, we show empirically that simulation-derived performance guarantees transfer to the actual vehicle when executing a policy optimized using a deep stochastic dynamics model fit to vehicle data. 
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  2. This work studies an approach for computing provably robust control laws for robotic systems operating in uncertain environments. We develop an actor-critic style policy search algorithm based on the idea of minimizing an upper confidence bound on the negative expected advantage of a control policy at each policy update iteration. This new algorithm is a reformulation of Probably-Approximately-Correct Robust Policy Search (PROPS) and, unlike PROPS, allows for both step-based evaluation and step-based sampling strategies in policy parameter space, enabled by the use of Generalized Advantage Estimation and Generalized Exploration. As a result, the new algorithm is more data efficient and is expected to compute higher quality policies faster. We empirically evaluate the algorithm in simulation on a challenging robot navigation task using a high-fidelity deep stochastic model of an agile ground vehicle and compare its performance to the original trajectory-based PROPS 
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