5 papers
Plug-and-Play Reweighting for Resilient Collaborative Decision-Making in Connected Autonomous Driving
Jiewen Liu, Rui Liu, Matthew Lee +3
Collaborative decision-making is a fundamental capability in multi-robot systems, such as connected autonomous vehicles. However, perceptual noise and adversarial attacks in collab…
Active Asymmetric Multi-Agent Multimodal Learning under Uncertainty
Rui Liu, Pratap Tokekar, Ming Lin
Multi-agent systems are increasingly equipped with heterogeneous multimodal sensors, enabling richer perception but introducing modality-specific and agent-dependent uncertainty. E…
CAML: Collaborative Auxiliary Modality Learning for Multi-Agent Systems
Rui Liu, Yu Shen, Peng Gao +2
Multi-modal learning has emerged as a key technique for improving performance across domains such as autonomous driving, robotics, and reasoning. However, in certain scenarios, par…
Adaptive Conformal Guidance for Learning under Uncertainty
Rui Liu, Peng Gao, Yu Shen +2
Learning with guidance has proven effective across a wide range of machine learning systems. Guidance may, for example, come from annotated datasets in supervised learning, pseudo-…
MMCD: Multi-Modal Collaborative Decision-Making for Connected Autonomy with Knowledge Distillation
Rui Liu, Zikang Wang, Peng Gao +3
Autonomous systems have advanced significantly, but challenges persist in accident-prone environments where robust decision-making is crucial. A single vehicle's limited sensor ran…