3 papers
cs.RO2026
Self-Evolutionary Replanning for Failure-Aware Motion Planning
Guoliang Li, Ruihua Han, Chengyang Li +5
Motion planners tuned under nominal conditions can fail when execution conditions change. Existing differentiable adaptation methods provide efficient online updates but remain con…
cs.RO2022
Are We Ready for Robust and Resilient SLAM? A Framework For Quantitative Characterization of SLAM Datasets
Islam Ali, Hong Zhang
Reliability of SLAM systems is considered one of the critical requirements in modern autonomous systems. This directed the efforts to developing many state-of-the-art systems, crea…
cs.AI2019
Single-step Options for Adversary Driving
Nazmus Sakib, Hengshuai Yao, Hong Zhang +1
In this paper, we use reinforcement learning for safety driving in adversary settings. In our work, the knowledge in state-of-art planning methods is reused by single-step options…