327 citations · 1.3k across the 66 of their papers we have counts for
10 papers · 1 filter
Lyapunov-stable Neural Control for State and Output Feedback: A Novel Formulation
Lujie Yang, Hongkai Dai, Zhouxing Shi +3
Learning-based neural network (NN) control policies have shown impressive empirical performance in a wide range of tasks in robotics and control. However, formal (Lyapunov) stabili…
Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching
H. J. Terry Suh, Glen Chou, Hongkai Dai +3
Gradient-based methods enable efficient search capabilities in high dimensions. However, in order to apply them effectively in offline optimization paradigms such as offline Reinfo…
Cost-Driven Representation Learning for Linear Quadratic Gaussian Control: Part I
Yi Tian, Kaiqing Zhang, Russ Tedrake +1
We study the task of learning state representations from potentially high-dimensional observations, with the goal of controlling an unknown partially observable system. We pursue a…
Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision?
Lirui Wang, Kaiqing Zhang, Yunzhu Li +2
Decentralized learning has been advocated and widely deployed to make efficient use of distributed datasets, with an extensive focus on supervised learning (SL) problems. Unfortuna…
Do Differentiable Simulators Give Better Policy Gradients?
H. J. Terry Suh, Max Simchowitz, Kaiqing Zhang +1
Differentiable simulators promise faster computation time for reinforcement learning by replacing zeroth-order gradient estimates of a stochastic objective with an estimate based o…
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems
Aman Sinha, Matthew O'Kelly, Russ Tedrake +1
Learning-based methodologies increasingly find applications in safety-critical domains like autonomous driving and medical robotics. Due to the rare nature of dangerous events, rea…
FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis
Aman Sinha, Matthew O'Kelly, Hongrui Zheng +3
Balancing performance and safety is crucial to deploying autonomous vehicles in multi-agent environments. In particular, autonomous racing is a domain that penalizes safe but conse…
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation
Matthew O'Kelly, Aman Sinha, Hongseok Namkoong +2
While recent developments in autonomous vehicle (AV) technology highlight substantial progress, we lack tools for rigorous and scalable testing. Real-world testing, the $\textit{de…
Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids
Yunzhu Li, Jiajun Wu, Russ Tedrake +2
Real-life control tasks involve matters of various substances---rigid or soft bodies, liquid, gas---each with distinct physical behaviors. This poses challenges to traditional rigi…
Evaluating Robustness of Neural Networks with Mixed Integer Programming
Vincent Tjeng, Kai Xiao, Russ Tedrake
Neural networks have demonstrated considerable success on a wide variety of real-world problems. However, networks trained only to optimize for training accuracy can often be foole…