Robot Trajectory Optimization Using Approximate Inference

author: Marc Toussaint, Machine Learning and Intelligent Data Analysis Group, TU Berlin
published: Aug. 26, 2009,   recorded: June 2009,   views: 4421

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The general stochastic optimal control (SOC) problem in robotics scenarios is often too complex to be solved exactly and in near real time. A classical approximate solution is to first compute an optimal (deterministic) trajectory and then solve a local linear-quadratic-gaussian (LQG) perturbation model to handle the system stochasticity. We present a new algorithm for this approach which improves upon previous algorithms like iLQG. We consider a probabilistic model for which the maximum likelihood (ML) trajectory coincides with the optimal trajectory and which, in the LQG case, reproduces the classical SOC solution. The algorithm then utilizes approximate inference methods (similar to expectation propagation) that efficiently generalize to non-LQG systems. We demonstrate the algorithm on a simulated 39-DoF humanoid robot.

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Reviews and comments:

Comment1 Ashish Jain, July 23, 2010 at 4:07 p.m.:

The slides are not clear in the video. It'll be nice if the author of this video could also post the corresponding slides.

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