16 citations · 33 across the 6 of their papers we have counts for
6 papers
Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specifications
Jiangwei Wang, Shuo Yang, Ziyan An +5
Reward design is a key component of deep reinforcement learning, yet some tasks and designer's objectives may be unnatural to define as a scalar cost function. Among the various te…
Safe Perception-Based Control under Stochastic Sensor Uncertainty using Conformal Prediction
Shuo Yang, George J. Pappas, Rahul Mangharam +1
We consider perception-based control using state estimates that are obtained from high-dimensional sensor measurements via learning-enabled perception maps. However, these percepti…
MEGA-DAgger: Imitation Learning with Multiple Imperfect Experts
Xiatao Sun, Shuo Yang, Mingyan Zhou +2
Imitation learning has been widely applied to various autonomous systems thanks to recent development in interactive algorithms that address covariate shift and compounding errors…
A Benchmark Comparison of Imitation Learning-based Control Policies for Autonomous Racing
Xiatao Sun, Mingyan Zhou, Zhijun Zhuang +3
Autonomous racing with scaled race cars has gained increasing attention as an effective approach for developing perception, planning and control algorithms for safe autonomous driv…
Differentiable Safe Controller Design through Control Barrier Functions
Shuo Yang, Shaoru Chen, Victor M. Preciado +1
Learning-based controllers, such as neural network (NN) controllers, can show high empirical performance but lack formal safety guarantees. To address this issue, control barrier f…
Fast Contact-Implicit Model-Predictive Control
Simon Le Cleac'h, Taylor Howell, Shuo Yang +5
We present a general approach for controlling robotic systems that make and break contact with their environments. Contact-implicit model predictive control (CI-MPC) generalizes li…