59 citations · 454 across the 124 of their papers we have counts for
11 papers · 1 filter
SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments
Hanjiang Hu, Baoquan Yang, Zhijian Qiao +6
Different environments pose a great challenge to the outdoor robust visual perception for long-term autonomous driving, and the generalization of learning-based algorithms on diffe…
Rare-Event Simulation for Neural Network and Random Forest Predictors
Yuanlu Bai, Zhiyuan Huang, Henry Lam +1
We study rare-event simulation for a class of problems where the target hitting sets of interest are defined via modern machine learning tools such as neural networks and random fo…
Constrained Model-based Reinforcement Learning with Robust Cross-Entropy Method
Zuxin Liu, Hongyi Zhou, Baiming Chen +3
This paper studies the constrained/safe reinforcement learning (RL) problem with sparse indicator signals for constraint violations. We propose a model-based approach to enable RL…
Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation
Wenhao Ding, Baiming Chen, Bo Li +2
Existing neural network-based autonomous systems are shown to be vulnerable against adversarial attacks, therefore sophisticated evaluation on their robustness is of great importan…
MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments
Zuxin Liu, Baiming Chen, Hongyi Zhou +3
Multi-agent navigation in dynamic environments is of great industrial value when deploying a large scale fleet of robot to real-world applications. This paper proposes a decentrali…
Robust Unsupervised Learning of Temporal Dynamic Interactions
Aritra Guha, Rayleigh Lei, Jiacheng Zhu +2
Robust representation learning of temporal dynamic interactions is an important problem in robotic learning in general and automated unsupervised learning in particular. Temporal d…
Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems
Mansur Arief, Zhiyuan Huang, Guru Koushik Senthil Kumar +5
Evaluating the reliability of intelligent physical systems against rare safety-critical events poses a huge testing burden for real-world applications. Simulation provides a useful…
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
Mengdi Xu, Wenhao Ding, Jiacheng Zhu +3
Continuously learning to solve unseen tasks with limited experience has been extensively pursued in meta-learning and continual learning, but with restricted assumptions such as ac…
Delay-Aware Model-Based Reinforcement Learning for Continuous Control
Baiming Chen, Mengdi Xu, Liang Li +1
Action delays degrade the performance of reinforcement learning in many real-world systems. This paper proposes a formal definition of delay-aware Markov Decision Process and prove…
Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive Environments
Baiming Chen, Mengdi Xu, Zuxin Liu +2
Action and observation delays exist prevalently in the real-world cyber-physical systems which may pose challenges in reinforcement learning design. It is particularly an arduous t…
Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method
Wenhao Ding, Baiming Chen, Minjun Xu +1
Long-tail and rare event problems become crucial when autonomous driving algorithms are applied in the real world. For the purpose of evaluating systems in challenging settings, we…