Exo-ViHa: A Cross-Platform Exoskeleton System with Visual and Haptic Feedback for Efficient Dexterous Skill Learning

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.

AVR: Active Vision-Driven Robotic Precision Manipulation with Viewpoint and Focal Length Optimization

Robotic manipulation within dynamic environments presents challenges to precise control and adaptability. Traditional fixed-view camera systems face challenges adapting to change viewpoints and scale variations, limiting perception and manipulation precision. To tackle these issues, we propose the Active Vision-driven Robotic (AVR) framework, a teleoperation hardware solution that supports dynamic viewpoint and dynamic focal length adjustments to continuously center targets and maintain optimal scale, accompanied by a corresponding algorithm that effectively enhances the success rates of various operational tasks. Using the RoboTwin platform with a real-time image processing plugin, AVR framework improves task success rates by 5%-16% on five manipulation tasks. Physical deployment on a dual-arm system demonstrates in collaborative tasks and 36% precision in screwdriver insertion, outperforming baselines by over 25%. Experimental results confirm that AVR framework enhances environmental perception, manipulation repeatability (40% $\le $1 cm error), and robustness in complex scenarios, paving the way for future robotic precision manipulation methods in the pursuit of human-level robot dexterity and precision.

Dual-modal Tactile E-skin: Enabling Bidirectional Human-Robot Interaction via Integrated Tactile Perception and Feedback

To foster an immersive and natural human-robot interaction, the implementation of tactile perception and feedback becomes imperative, effectively bridging the conventional sensory gap. In this paper, we propose a dual-modal electronic skin (e-skin) that integrates magnetic tactile sensing and vibration feedback for enhanced human-robot interaction. The dual-modal tactile e-skin offers multi-functional tactile sensing and programmable haptic feedback, underpinned by a layered structure comprised of flexible magnetic films, soft silicone, a Hall sensor and actuator array, and a microcontroller unit. The e-skin captures the magnetic field changes caused by subtle deformations through Hall sensors, employing deep learning for accurate tactile perception. Simultaneously, the actuator array generates mechanical vibrations to facilitate haptic feedback, delivering diverse mechanical stimuli. Notably, the dual-modal e-skin is capable of transmitting tactile information bidirectionally, enabling object recognition and fine-weighing operations. This bidirectional tactile interaction framework will enhance the immersion and efficiency of interactions between humans and robots.

SATac: A Thermoluminescence Enabled Tactile Sensor for Concurrent Perception of Temperature, Pressure, and Shear

Most vision-based tactile sensors use elastomer deformation to infer tactile information, which can not sense some modalities, like temperature. As an important part of human tactile perception, temperature sensing can help robots better interact with the environment. In this work, we propose a novel multimodal vision-based tactile sensor, SATac, which can simultaneously perceive information of temperature, pressure, and shear. SATac utilizes thermoluminescence of strontium aluminate (SA) to sense a wide range of temperatures with exceptional resolution. Additionally, the pressure and shear can also be perceived by analyzing Voronoi diagram. A series of experiments are conducted to verify the performance of our proposed sensor. We also discuss the possible application scenarios and demonstrate how SATac could benefit robot perception capabilities.