6 accepted papers
Dexterous in-hand telemanipulation demands precise control and realistic haptic feedback to achieve stable and intuitive human–robot interaction. Existing systems often emphasize isolated control policies or unidirectional force feedback, limiting performance in tasks that require coordinated bidire…
While Large Language Models (LLMs) demonstrate immense potential for automating integrated circuit (IC) development, their practical deployment is fundamentally limited by restricted context windows. Existing context-extension methods struggle to achieve effective semantic modeling and thorough mult
Dexterous telemanipulation critically relies on the continuous and stable tracking of the human operator’s commands to ensure robust operation. Vison-based tracking methods are widely used but have low stability due to anomalies such as occlusions, inadequate lighting, and loss of sight. Traditional
Tactile sensors can significantly enhance the perception of humanoid robotics systems by providing contact information that facilitates human-like interactions. However, existing commercial tactile sensors focus on improving the resolution and sensitivity of single-modal detection with high-cost com
Pouring fluids is a routine task for humans but challenging for high-DoF robots, particularly given fluid simulation’s computational demands while training policies. In this paper, we propose DexPour, a novel reinforcement learning method with hierarchical rewards and Approximated Proxy Abstraction
Large-scale, diverse datasets are essential for training robust learning-based robotic manipulation models; however, their acquisition typically requires controlled environments and specialized hardware in research laboratories. This paper presents VRobotix, a virtual reality (VR)-based framework th