Reinforcement Learning to Enable Robust Robotic Model Predictive Control
Grossman, Lev Jacob
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CitationGrossman, Lev Jacob. 2020. Reinforcement Learning to Enable Robust Robotic Model Predictive Control. Bachelor's thesis, Harvard College.
AbstractTraditional methods of robotic planning and trajectory optimization often break down when environmental conditions change, real-world noise is introduced, or when rewards become sparse. Consequently, much of the work involved in calculating trajectories is done not by algorithms, but by hand: tuning cost functions and engineering rewards. This thesis seeks to minimize this human effort by building on prior work combining both model-based and model- free methods within an actor-critic framework. This specific synergy allows for the automatic learning of cost functions expressive enough to enable robust robotic planning. This thesis proposes a novel algorithm, “Reference-Guided, Value-Based MPC,” which combines model predictive control (MPC) and reinforcement learning (RL) to compute feasible trajectories for a robotic arm. The algorithm does this while 1) achieving an almost 50% higher planning success rate than standard MPC, 2) solving in sparse environments considered unsolvable by current state of the art algorithms, and 3) generalizing its solutions to different environment initializations.
Citable link to this pagehttps://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37364714
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