DelTact: A Vision-based Tactile Sensor Using Dense Color Pattern

Tactile sensing is an essential perception for robots to complete dexterous tasks. As a promising tactile sensing technique, vision-based tactile sensors have been developed to improve robot performance in manipulation and grasping. Here we propose a new design of vision-based tactile sensor, DelTact, with its high-resolution sensing abilities of surface contact measurement. The sensor uses a modular hardware architecture for compactness whilst maintaining a robust overall design. Moreover, it adopts an improved dense random color pattern based on the previous version to achieve high accuracy of contact deformation tracking. In particular, we optimize the color pattern generation process and select the appropriate pattern for coordinating with a dense optical flow algorithm in a real-world experimental sensory setting using various objects for contact. The optical flow obtained from the raw image is processed to determine shape and force distribution on the contact surface. This sensor can be easily integrated with a parallel gripper where experimental results using qualitative and quantitative analysis demonstrate that the sensor is capable of providing tactile measurements with high temporal and spatial resolution.

A Tactile Sensing Foot for Single Robot Leg Stabilization

Tactile sensing on human feet is crucial for motion control, however, has not been explored in robotic counterparts. This work is dedicated to endowing tactile sensing to legged robot's feet and showing that a single-legged robot can be stabilized with only tactile sensing signals from its foot. We propose a robot leg with a novel vision-based tactile sensing foot system and implement a processing algorithm to extract contact information for feedback control in stabilizing tasks. A pipeline to convert images of the foot skin into high-level contact information using a deep learning framework is presented. The leg was quantitatively evaluated in a stabilization task on a tilting surface to show that the tactile foot was able to estimate both the surface tilting angle and the foot poses. Feasibility and effectiveness of the tactile system were investigated qualitatively in comparison with conventional single-legged robotic systems using inertia measurement units (IMU). Experiments demonstrate the capability of vision-based tactile sensors in assisting legged robots to maintain stability on unknown terrains and the potential for regulating more complex motions for humanoid robots.