2 citations · 6 across the 8 of their papers we have counts for
8 papers
Robustness of Robotic Manipulation: Foundations and Frontiers
Yifei Dong, Zhanyi Sun, Lujie Yang +5
Humans and animals exhibit remarkable robustness in physical manipulation, yet robots remain far behind. Progress toward human-level manipulation robustness is hindered by the abse…
A New Semidefinite Relaxation for Linear and Piecewise-Affine Optimal Control with Time Scaling
Lujie Yang, Tobia Marcucci, Pablo A. Parrilo +1
We introduce a semidefinite relaxation for optimal control of linear systems with time scaling. These problems are inherently nonconvex, since the system dynamics involves bilinear…
Physics-Driven Data Generation for Contact-Rich Manipulation via Trajectory Optimization
Lujie Yang, H. J. Terry Suh, Tong Zhao +5
We present a low-cost data generation pipeline that integrates physics-based simulation, human demonstrations, and model-based planning to efficiently generate large-scale, high-qu…
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…
Approximate Optimal Controller Synthesis for Cart-Poles and Quadrotors via Sums-of-Squares
Lujie Yang, Hongkai Dai, Alexandre Amice +1
Sums-of-squares (SOS) optimization is a promising tool to synthesize certifiable controllers for nonlinear dynamical systems. Building upon prior works, we demonstrate that SOS can…
Global Planning for Contact-Rich Manipulation via Local Smoothing of Quasi-dynamic Contact Models
Tao Pang, H. J. Terry Suh, Lujie Yang +1
The empirical success of Reinforcement Learning (RL) in the setting of contact-rich manipulation leaves much to be understood from a model-based perspective, where the key difficul…
Lyapunov-stable neural-network control
Hongkai Dai, Benoit Landry, Lujie Yang +2
Deep learning has had a far reaching impact in robotics. Specifically, deep reinforcement learning algorithms have been highly effective in synthesizing neural-network controllers…