Probabilistic inference and influence diagrams
Operations Research
Decision making using probabilistic inference methods
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Using expectation-maximization for reinforcement learning
Neural Computation
Expectation Propagation for approximate Bayesian inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
EP for Efficient Stochastic Control with Obstacles
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
A dynamical system approach to realtime obstacle avoidance
Autonomous Robots
A policy-blending formalism for shared control
International Journal of Robotics Research
CHOMP: Covariant Hamiltonian optimization for motion planning
International Journal of Robotics Research
International Journal of Robotics Research
Path integral control by reproducing kernel Hilbert space embedding
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Hi-index | 0.00 |
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.