173 citations · 876 across the 56 of their papers we have counts for
4 papers · 1 filter
Learning Stabilizable Dynamical Systems via Control Contraction Metrics
Sumeet Singh, Vikas Sindhwani, Jean-Jacques E. Slotine +1
We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics. The key idea is to develop a new control-theoretic regu…
Optimizing Simulations with Noise-Tolerant Structured Exploration
Krzysztof Choromanski, Atil Iscen, Vikas Sindhwani +2
We propose a simple drop-in noise-tolerant replacement for the standard finite difference procedure used ubiquitously in blackbox optimization. In our approach, parameter perturbat…
Learning Contracting Vector Fields For Stable Imitation Learning
Vikas Sindhwani, Stephen Tu, Mohi Khansari
We propose a new non-parametric framework for learning incrementally stable dynamical systems x' = f(x) from a set of sampled trajectories. We construct a rich family of smooth vec…
Structured Evolution with Compact Architectures for Scalable Policy Optimization
Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani +2
We present a new method of blackbox optimization via gradient approximation with the use of structured random orthogonal matrices, providing more accurate estimators than baselines…