5 papers
Efficient Model-Based Reinforcement Learning for Robot Control via Online Optimization
Fang Nan, Hao Ma, Qinghua Guan +3
We present an online model-based reinforcement learning algorithm suitable for controlling complex robotic systems directly in the real world. Unlike prevailing sim-to-real pipelin…
Stable at Any Speed: Speed-Driven Multi-Object Tracking with Learnable Kalman Filtering
Yan Gong, Mengjun Chen, Hao Liu +5
Multi-object tracking (MOT) enables autonomous vehicles to continuously perceive dynamic objects, supplying essential temporal cues for prediction, behavior understanding, and safe…
Constraint-Aware Diffusion Guidance for Robotics: Real-Time Obstacle Avoidance for Autonomous Racing
Hao Ma, Sabrina Bodmer, Andrea Carron +2
Diffusion models hold great potential in robotics due to their ability to capture complex, high-dimensional data distributions. However, their lack of constraint-awareness limits t…
Stochastic Online Optimization for Cyber-Physical and Robotic Systems
Hao Ma, Melanie Zeilinger, Michael Muehlebach
We propose a novel gradient-based online optimization framework for solving stochastic programming problems that frequently arise in the context of cyber-physical and robotic syste…
Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation
Zhen Liu, Hang Gao, Hao Ma +5
Autonomous vehicle (AV) evaluation has been the subject of increased interest in recent years both in industry and in academia. This paper focuses on the development of a novel fra…