3 accepted papers
Reinforcement learning with verifiable rewards (RLVR) has advanced the reasoning capabilities of large language models. Howerer, existing methods rely solely on outcome rewards, without explicitly optimizing verification or leveraging reliable signals from realistic environments, leading to unreliab…
Multi-modal fusion perception enhances robotic performance in complex tasks by providing more comprehensive information than single modality. While tactile and proprioceptive sensing are effective for direct contact tasks like grasping, current research mainly focuses on vision-language fusion, negl
Multi-modal perception plays a crucial role in preventing deformation and damage during the robotic manipulation of deformable objects. However, integrating new heterogeneous modalities into existing robotic perception frameworks remains a significant challenge, primarily due to the need for massive