3 papers
cs.RO2025
TP-MDDN: Task-Preferenced Multi-Demand-Driven Navigation with Autonomous Decision-Making
Shanshan Li, Da Huang, Yu He +3
In daily life, people often move through spaces to find objects that meet their needs, posing a key challenge in embodied AI. Traditional Demand-Driven Navigation (DDN) handles one…
cs.CV2025
Topology-Aware CLIP Few-Shot Learning
Dazhi Huang
Efficiently adapting large Vision-Language Models (VLMs) like CLIP for few-shot learning poses challenges in balancing pre-trained knowledge retention and task-specific adaptation.…
cs.LG2025
Towards a Reward-Free Reinforcement Learning Framework for Vehicle Control
Jielong Yang, Daoyuan Huang
Reinforcement learning plays a crucial role in vehicle control by guiding agents to learn optimal control strategies through designing or learning appropriate reward signals. Howev…