13 citations · 27 across the 27 of their papers we have counts for
8 papers · 1 filter
Optimal Driver Warning Generation in Dynamic Driving Environment
Chenran Li, Aolin Xu, Enna Sachdeva +2
The driver warning system that alerts the human driver about potential risks during driving is a key feature of an advanced driver assistance system. Existing driver warning techno…
DexH2R: Task-oriented Dexterous Manipulation from Human to Robots
Shuqi Zhao, Xinghao Zhu, Yuxin Chen +5
Dexterous manipulation is a critical aspect of human capability, enabling interaction with a wide variety of objects. Recent advancements in learning from human demonstrations and…
X-MOBILITY: End-To-End Generalizable Navigation via World Modeling
Wei Liu, Huihua Zhao, Chenran Li +5
General-purpose navigation in challenging environments remains a significant problem in robotics, with current state-of-the-art approaches facing myriad limitations. Classical appr…
WOMD-Reasoning: A Large-Scale Dataset for Interaction Reasoning in Driving
Yiheng Li, Cunxin Fan, Chongjian Ge +9
Language models uncover unprecedented abilities in analyzing driving scenarios, owing to their limitless knowledge accumulated from text-based pre-training. Naturally, they should…
Residual-MPPI: Online Policy Customization for Continuous Control
Pengcheng Wang, Chenran Li, Catherine Weaver +4
Policies developed through Reinforcement Learning (RL) and Imitation Learning (IL) have shown great potential in continuous control tasks, but real-world applications often require…
MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention
Yuxin Chen, Chen Tang, Jianglan Wei +6
Aligning robot behavior with human preferences is crucial for deploying embodied AI agents in human-centered environments. A promising solution is interactive imitation learning fr…
Adaptive Energy Regularization for Autonomous Gait Transition and Energy-Efficient Quadruped Locomotion
Boyuan Liang, Lingfeng Sun, Xinghao Zhu +6
In reinforcement learning for legged robot locomotion, crafting effective reward strategies is crucial. Pre-defined gait patterns and complex reward systems are widely used to stab…
DrPlanner: Diagnosis and Repair of Motion Planners for Automated Vehicles Using Large Language Models
Yuanfei Lin, Chenran Li, Mingyu Ding +3
Motion planners are essential for the safe operation of automated vehicles across various scenarios. However, no motion planning algorithm has achieved perfection in the literature…