Imitation learning has emerged as a powerful paradigm for robot skills learning. However, traditional data collection systems for dexterous manipulation face challenges, including a lack of balance between acquisition efficiency, consistency, and accuracy. To address these issues, we introduce Exo-ViHa, an innovative 3D-printed exoskeleton system that enables users to collect data from a first-person perspective while providing real-time haptic feedback. This system combines a 3D-printed modular structure with a slam camera, a motion capture glove, and a wrist-mounted camera. Various dexterous hands can be installed at the end, enabling it to simultaneously collect the posture of the end effector, hand movements, and visual data. By leveraging the first-person perspective and direct interaction, the exoskeleton enhances the task realism and haptic feedback, improving the consistency between demonstrations and actual robot deployments. In addition, it has cross-platform compatibility with various robotic arms and dexterous hands. Experiments show that the system can significantly improve the success rate and efficiency of data collection for dexterous manipulation tasks. Webpage: https://exo-viha2025.github.io/.

Visuotactile sensors provide high-resolution tactile information but are incapable of perceiving the material features of objects. We present UltraTac, an integrated sensor that combines visuotactile imaging with ultrasound sensing through a coaxial optoacoustic architecture. The design shares structural components and achieves consistent sensing regions for both modalities. Additionally, we incorporate acoustic matching into the traditional visuotactile sensor structure, enabling the integration of the ultrasound sensing modality without compromising visuotactile performance. Through tactile feedback, we can dynamically adjust the operating state of the ultrasound module to achieve more flexible functional coordination. Systematic experiments demonstrate three key capabilities: proximity sensing in the 3–8 cm range (R2= 0.99), material classification (average accuracy: 99.20%), and texture-material dual-mode object recognition achieves 92.11% accuracy on a 15-class task. Finally, we integrate the sensor into a robotic manipulation system to concurrently detect container surface patterns and internal content, which verifies its promising potential for advanced human-machine interaction and precise robotic manipulation.

In the pursuit of deeper immersion in human-machine interaction, achieving higher-dimensional tactile input and output on a single interface has become a key research focus. This study introduces the Visual-Electronic Tactile (VET) System, which builds upon vision-based tactile sensors (VBTS) and integrates electrical stimulation feedback to enable bidirectional tactile communication. We propose and implement a system framework that seamlessly integrates an electrical stimulation film with VBTS using a screen-printing preparation process, eliminating interference from traditional methods. While VBTS captures multi-dimensional input through visuotactile signals, electrical stimulation feedback directly stimulates neural pathways, preventing interference with visuotactile information. The potential of the VET system is demonstrated through experiments on finger electrical stimulation sensitivity zones, as well as applications in interactive gaming and robotic arm teleoperation. This system paves the way for new advancements in bidirectional tactile interaction and its broader applications.

Robot-assisted vascular interventional surgery can isolate interventionists and X-ray radiation, and improve surgical accuracy. However, the leader side outside the operating room still has problems such as incomplete collection of operating information and unrealistic tactile feedback. The main objective of this paper is to design a haptic interface that can simultaneously capture the force-position information of the interventionists and generate force to assist the interventionists in performing surgeries on the leader side. It can capture the interventionists’ delivery displacement, twisting angle, clamping force, and provide real-time force feedback. A leader-follower bidirectional force feedback control strategy was proposed. Based on this strategy, on the one hand, the interventionist perceives the multi-modal information fed back from the follower side, makes judgments, and actively adjusts the surgical operation. On the other hand, the interventionist controls the grasping state of the instruments remotely to control the safety operating force threshold. Finally, the experimental setup was built and a series of evaluation experiments were performed. The experimental results verified the feasibility of the designed haptic interface. It can generate dynamic and accurate force feedback and realize leader-follower grasping force control.