4 accepted papers
Accurate in-hand pose estimation is essential for dexterous robotic manipulation but remains fragile under severe visual occlusion (<inline-formula><tex-math notation="LaTeX">$>$</tex-math></inline-formula>50%) and intermittent tactile contact. Existing visuo-tactile fusion methods treat vision and
Offline reinforcement learning strives to enable agents to effectively utilize pre-collected offline datasets for learning. Such an offline setup tremendously mitigates the problems of online reinforcement learning algorithms in real-world applications, particularly in scenarios where interactions a
The current robotic sliding detection tasks lack an effective contextual reasoning mechanism, which leads to inaccurate decision-making in unknown environments. To address this issue, we propose MambaSlip, which leverages the advantages of large language models (LLMs) in context understanding and re
Sample usage efficiency is an important factor affecting the convergence speed of multi-agent deep reinforcement learning (MADRL) algorithms. Most existing experience replay (ER) methods manually select experience samples to update the agent's policy. It is difficult to give suitable and efficient e