6 accepted papers
Autonomous execution of long-horizon, contact-rich manipulation tasks traditionally requires extensive real-world data and expert engineering, posing significant cost and scalability challenges. This paper proposes a novel framework integrating hierarchical semantic decomposition, reinforcement lear
In the field of robotic manipulation, traditional methods lack the flexibility required to meet the demands of diverse applications. Consequently, researchers have increasingly focused on developing more general techniques, particularly for long-horizon and gentle manipulation, to enhance the manipu
Physics-based humanoid control relies on training with motion datasets that have diverse data distributions. However, the fixed difficulty distribution of datasets limits the performance ceiling of the trained control policies. Additionally, the method of acquiring high-quality data through professi
Tactile sensing is essential for robots to achieve human-like gentle manipulation capabilities. However, existing Vision-Language-Action (VLA) models struggle to exploit tactile feedback for gentle manipulation due to the scarcity of aligned vision-tactile-language data and the lack of effective clo…
In the field of dexterous robotic manipulation, integrating visual and tactile modalities to inform manipulation policies presents significant challenges, especially in non-contact scenarios where reliance on tactile perception can be inadequate. Visual affordance techniques currently offer effectiv
An increasing number of robotic manipulation tasks now use optical tactile sensors to provide tactile feedback, making tactile servo control a crucial aspect of robotic operations. This paper presents a rapid tactile transfer framework (RTTF) that achieves optical-tactile image sim2real transfer and…