• Publications
  • Influence
MuJoCo: A physics engine for model-based control
A new physics engine tailored to model-based control, based on the modern velocity-stepping approach which avoids the difficulties with spring-dampers, which can compute both forward and inverse dynamics.
Optimal feedback control as a theory of motor coordination
This work shows that the optimal strategy in the face of uncertainty is to allow variability in redundant (task-irrelevant) dimensions, and proposes an alternative theory based on stochastic optimal feedback control, which emerges naturally from this framework.
Optimality principles in sensorimotor control
  • E. Todorov
  • Psychology
    Nature Neuroscience
  • 1 September 2004
This work has redefined optimality in terms of feedback control laws, and focused on the mechanisms that generate behavior online, allowing researchers to fit previously unrelated concepts and observations into what may become a unified theoretical framework for interpreting motor function.
Linearly-solvable Markov decision problems
A class of MPDs which greatly simplify Reinforcement Learning, which have discrete state spaces and continuous control spaces and enable efficient approximations to traditional MDPs.
Stochastic Optimal Control and Estimation Methods Adapted to the Noise Characteristics of the Sensorimotor System
Under this extended noise model, a coordinate-descent algorithm guaranteed to converge to a feedback control law and a nonadaptive linear estimator optimal with respect to each other is derived.
A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems
We present an iterative linear-quadratic-Gaussian method for locally-optimal feedback control of nonlinear stochastic systems subject to control constraints. Previously, similar methods have been
Efficient computation of optimal actions
  • E. Todorov
  • Computer Science
    Proceedings of the National Academy of Sciences
  • 14 July 2009
This work proposes a more structured formulation that greatly simplifies the construction of optimal control laws in both discrete and continuous domains, and enables computations that were not possible before.
Synthesis and stabilization of complex behaviors through online trajectory optimization
We present an online trajectory optimization method and software platform applicable to complex humanoid robots performing challenging tasks such as getting up from an arbitrary pose on the ground
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
This work shows that model-free DRL with natural policy gradients can effectively scale up to complex manipulation tasks with a high-dimensional 24-DoF hand, and solve them from scratch in simulated experiments.
Iterative Linear Quadratic Regulator Design for Nonlinear Biological Movement Systems
The Iterative Linear Quadratic Regulator method is applied to a musculo-skeletal arm model with 10 state dimensions and 6 controls, and is used to compute energy-optimal reaching movements.