Search Results for author:
Found 9 papers, 3 papers with code
Deep Forward-Backward SDEs for Min-max Control
This paper presents a novel approach to numerically solve stochastic differential games for nonlinear systems.
NOVAS: Non-convex Optimization via Adaptive Stochastic Search for End-to-End Learning and Control
In this work we propose the use of adaptive stochastic search as a building block for general, non-convex optimization operations within deep neural network architectures.
Safe Optimal Control Using Stochastic Barrier Functions and Deep Forward-Backward SDEs
This paper introduces a new formulation for stochastic optimal control and stochastic dynamic optimization that ensures safety with respect to state and control constraints.
Multi-agent Deep FBSDE Representation For Large Scale Stochastic Differential Games
In this paper we present a deep learning framework for solving large-scale multi-agent non-cooperative stochastic games using fictitious play.
Policy Transfer via Kinematic Domain Randomization and Adaptation
Transferring reinforcement learning policies trained in physics simulation to the real hardware remains a challenge, known as the "sim-to-real" gap.
Large-Scale Multi-Agent Deep FBSDEs
We showcase superior performance of our framework over the state-of-the-art deep fictitious play algorithm on an inter-bank lending/borrowing problem in terms of multiple metrics.
Task-Specific Design Optimization and Fabrication for Inflated-Beam Soft Robots with Growable Discrete Joints
Soft robot serial chain manipulators with the capability for growth, stiffness control, and discrete joints have the potential to approach the dexterity of traditional robot arms, while improving safety, lowering cost, and providing an increased workspace, with potential application in home environments.
Robotics
Fast and Feature-Complete Differentiable Physics for Articulated Rigid Bodies with Contact
We present a fast and feature-complete differentiable physics engine, Nimble (nimblephysics. org), that supports Lagrangian dynamics and hard contact constraints for articulated rigid body simulation.
Deep $\mathcal{L}^1$ Stochastic Optimal Control Policies for Planetary Soft-landing
In this paper, we introduce a novel deep learning based solution to the Powered-Descent Guidance (PDG) problem, grounded in principles of nonlinear Stochastic Optimal Control (SOC) and Feynman-Kac theory.
