4 accepted papers
In robotic skill acquisition, rapid policy learning remains challenging due to high-dimensional state-action spaces and inefficient exploration in the early stage of training cite{p1}. Although the pre-trained OpenVLA model exhibits cross-task generalization and can generate goal-directed actions fo…
Reinforcement learning (RL) has emerged as a powerful framework to improve the reasoning performance of large language models (LLMs), with approaches such as Group Relative Policy Optimization (GRPO) showing promising results. However, GRPO and its variants struggle with collapsed groups (i.e., all-
Medical time-series data play a vital role in disease diagnosis but suffer from limited labeled samples and single-center bias, which hinder model generalization and lead to overfitting. To address these challenges, we propose DAAC (Discrepancy-Aware Adaptive Contrastive learning), a learnable multi…
Snap-fit peg-in-hole assembly widely exists in both industry and daily life, especially for consumer electronics. The buckle mechanism leads to a damping zone inside the port where insertion force needs to be increased. It is much difficult to automate this process by robots, for size and clearance…