Approximate Inference Control
published: Jan. 19, 2010, recorded: December 2009, views: 4758
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Approximate Inference Control (AICO) is a method for solving Stochastic Optimal Control (SOC) problems. The general idea is to think of control as the problem of computing a posterior over trajectories and control signals conditioned on constraints and goals. Since exact inference is infeasible in realistic scenarios, the key for high-speed planning and control algorithms is the choice of approximations. In this talk I will introduce to the general approach, discuss its intimate relations to DDP and the current research on Kalman's duality, and discuss the approximations that we use to get towards real-time planning in high-dimensional robotic systems. I will also mention recent work on using Expectation Propagation and truncated Gaussians for inference under hard constraints and limits as they typically arise in robotics (collision and joint limit constraints).
Download slides: nipsworkshops09_toussaint_aic_01.pdf (857.7 KB)
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Reviews and comments:
Wow. I really like this topic. I think the main advantage of this is the integration ability of it. I mean, estimation, motion control and high level reasoning can all be formulated in the same framework.
I think the potential for using sample based representations to get more global solutions would be interesting subject for research...
Thanks for the great lecture
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