7 papers
Safe Overtaking for Autonomous Racing Using Hierarchical Optimization and Learning-Based Control
Hassan Jardali, Kai Yin, Lantao Liu
The paper introduces a hierarchical framework for autonomous racing overtaking that separates high‑level maneuver selection (via a mixed‑integer quadratic program) from low‑level s…
Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization
Alejandro Murillo-Gonzalez, Mahmoud Ali, Lantao Liu
Multi-objective reinforcement learning in robotic domains requires balancing complex, non-convex trade-offs between conflicting objectives. While linear scalarization methods provi…
Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration
Youwei Yu, Jionghao Wang, Zhengming Yu +2
Designing learnable information-theoretic objectives for robot exploration remains challenging. Such objectives aim to guide exploration toward data that reduces uncertainty in mod…
From Zero to High-Speed Racing: An Autonomous Racing Stack
Hassan Jardali, Durgakant Pushp, Youwei Yu +9
High-speed, head-to-head autonomous racing presents substantial technical and logistical challenges, including precise localization, rapid perception, dynamic planning, and real-ti…
Minimalistic Autonomous Stack for High-Speed Time-Trial Racing
Mahmoud Ali, Hassan Jardali, Youwei Yu +2
Autonomous racing has seen significant advancements, driven by competitions such as the Indy Autonomous Challenge (IAC) and the Abu Dhabi Autonomous Racing League (A2RL). However,…
ADEPT: Adaptive Diffusion Environment for Policy Transfer Sim-to-Real
Youwei Yu, Junhong Xu, Lantao Liu
Model-free reinforcement learning has emerged as a powerful method for developing robust robot control policies capable of navigating through complex and unstructured environments.…