Adaptive Probabilistic Trajectory Optimization via Efficient Approximate Inference

arXiv: Robotics, Volume abs/1608.06235, 2016.

Cited by: 7|Views43
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Abstract:

Robotic systems must be able to quickly and robustly make decisions when operating in uncertain and dynamic environments. While Reinforcement Learning (RL) can be used to compute optimal policies with little prior knowledge about the environment, it suffers from slow convergence. An alternative approach is Model Predictive Control (MPC), ...More

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