Search Results for author: Emanuel Todorov

Found 11 papers, 2 papers with code

Computing the Newton-step faster than Hessian accumulation

no code implementations2 Aug 2021 Akshay Srinivasan, Emanuel Todorov

In this paper, we show that given the computational graph of the function, this bound can be reduced to $O(m\tau^3)$, where $\tau, m$ are the width and size of a tree-decomposition of the graph.

Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control

no code implementations ICLR 2019 Kendall Lowrey, Aravind Rajeswaran, Sham Kakade, Emanuel Todorov, Igor Mordatch

We study how local trajectory optimization can cope with approximation errors in the value function, and can stabilize and accelerate value function learning.

Towards Generalization and Simplicity in Continuous Control

1 code implementation NeurIPS 2017 Aravind Rajeswaran, Kendall Lowrey, Emanuel Todorov, Sham Kakade

This work shows that policies with simple linear and RBF parameterizations can be trained to solve a variety of continuous control tasks, including the OpenAI gym benchmarks.

Continuous Control OpenAI Gym

Learning Dexterous Manipulation Policies from Experience and Imitation

no code implementations15 Nov 2016 Vikash Kumar, Abhishek Gupta, Emanuel Todorov, Sergey Levine

We demonstrate that such controllers can perform the task robustly, both in simulation and on the physical platform, for a limited range of initial conditions around the trained starting state.

Universal Convexification via Risk-Aversion

no code implementations3 Jun 2014 Krishnamurthy Dvijotham, Maryam Fazel, Emanuel Todorov

We develop a framework for convexifying a fairly general class of optimization problems.

Stochastic Optimization

MuJoCo: A physics engine for model-based control

1 code implementation IEEE/RSJ IROS 2012 Emanuel Todorov, Tom Erez, Yuval Tassa

To facilitate optimal control applications and in particular sampling and finite differencing, the dynamics can be evaluated for different states and controls in parallel.

Policy gradients in linearly-solvable MDPs

no code implementations NeurIPS 2010 Emanuel Todorov

We present policy gradient results within the framework of linearly-solvable MDPs.

Compositionality of optimal control laws

no code implementations NeurIPS 2009 Emanuel Todorov

We present a theory of compositionality in stochastic optimal control, showing how task-optimal controllers can be constructed from certain primitives.

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