2 citations · 2 across the 9 of their papers we have counts for
10 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…
Dexterity-BEV: Aligning 3D World and Actions for Generalizable Robot Policies Learning
Huayi Zhou, Wei Gao, Dekun Lu +12
End-to-end manipulation policies, combined with web-scale pretrained Vision-Language Models (VLMs), show the promise for generalizable and dexterous robotic manipulation. However,…
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…
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…
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-…
IMRL: Integrating Visual, Physical, Temporal, and Geometric Representations for Enhanced Food Acquisition
Rui Liu, Zahiruddin Mahammad, Amisha Bhaskar +1
Robotic assistive feeding holds significant promise for improving the quality of life for individuals with eating disabilities. However, acquiring diverse food items under varying…
Adaptive Visual Imitation Learning for Robotic Assisted Feeding Across Varied Bowl Configurations and Food Types
Rui Liu, Amisha Bhaskar, Pratap Tokekar
In this study, we introduce a novel visual imitation network with a spatial attention module for robotic assisted feeding (RAF). The goal is to acquire (i.e., scoop) food items fro…
LAVA: Long-horizon Visual Action based Food Acquisition
Amisha Bhaskar, Rui Liu, Vishnu D. Sharma +2
Robotic Assisted Feeding (RAF) addresses the fundamental need for individuals with mobility impairments to regain autonomy in feeding themselves. The goal of RAF is to use a robot…
Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis
Rui Liu, Anish Gupta, Erfaun Noorani +1
Reinforcement Learning (RL) has shown exceptional performance across various applications, enabling autonomous agents to learn optimal policies through interaction with their envir…