Zhe Su

Use of tactile feedback to control exploratory movements to characterize object compliance

Humans have been shown to be good at using active touch to perceive subtle differences in compliance. They tend to use highly stereotypical exploratory strategies, such as applying normal force to a surface. We developed similar exploratory and perceptual algorithms for a mechatronic robotic system (Barrett arm/hand system) equipped with liquid-filled, biomimetic tactile sensors (BioTac® from SynTouch LLC). The distribution of force on the fingertip was measured by the electrical resistance of the conductive liquid trapped between the elastomeric skin and a cluster of four electrodes on the flat fingertip surface of the rigid core of the BioTac. These signals provided closed-loop control of exploratory movements, while the distribution of skin deformations, measured by more lateral electrodes and by the hydraulic pressure, were used to estimate material properties of objects. With this control algorithm, the robot plus tactile sensor was able to discriminate the relative compliance of various rubber samples.

Force estimation and slip detection/classification for grip control using a biomimetic tactile sensor

We introduce and evaluate contact-based techniques to estimate tactile properties and detect manipulation events using a biomimetic tactile sensor. In particular, we estimate finger forces, and detect and classify slip events. In addition, we present a grip force controller that uses the estimation results to gently pick up objects of various weights and texture. The estimation techniques and the grip controller are experimentally evaluated on a robotic system consisting of Barrett arms and hands. Our results indicate that we are able to accurately estimate forces acting in all directions, detect the incipient slip, and classify slip with over 80% success rate.

Learning to Switch Between Sensorimotor Primitives Using Multimodal Haptic Signals

Most manipulation tasks can be decomposed into sequences of sensorimotor primitives. These primitives often end with characteristic sensory events, e.g., making or breaking contact, which indicate when the sensorimotor goal has been reached. In this manner, the robot can monitor the tactile signals to determine when to switch between primitives. In this paper, we present a framework for automatically segmenting contact-based manipulation tasks into sequences of sensorimotor primitives using multimodal haptic signals. These signals include both the robot’s end-effector position as well as the low- and high-frequency components of its tactile sensors. The resulting segmentation is used to learn to detect when the robot has reached a sensorimotor goal and it should therefore switch to the next primitive. The proposed framework was evaluated on guided peg-in-hole tasks. The experiments show that the framework can extract the subtasks of the manipulations and the sensorimotor goals can be accurately detected.

Contact localization on grasped objects using tactile sensing

Manipulation tasks often require robots to make contact between a grasped tool and another object in the robots environment. The ability to detect and estimate the positions and directions of these contact points is crucial for monitoring the progress of the task, and detecting failures. In this paper, we present a data-driven approach for detecting and localizing contacts between a grasped object and the environment using tactile sensing. We explore framing the contact localization as both a regression and a classification problem and train neural networks accordingly to estimate the contact parameters. We also compare the neural networks with Gaussian process regression and support vector machine classification with spatio-temporal hierarchical matching pursuit feature learning. We evaluate the presented approach using hundreds of contact events on eighteen objects with different shapes, sizes and material properties. The experiments show that the neural network approach can learn to localize contact events for individual objects with a mean absolute error of less than 2.5 cm for the positions and less than 10° for the directions.

Surface tilt perception with a biomimetic tactile sensor

Humans are known to be good at manipulating tools. To cope with disturbances and uncertainties from the external environment during such tasks, they must be able to perceive small changes in orientation or tilt of the tool using mechanoreceptors in the glabrous skin of the fingertips. We hypothesize that the most sensitive part of human fingers, a flat surface on the distal phalanx (called apical tuft) would be preferred for perceiving very fine tilts. In this paper, we used an experimental apparatus to quantify discrimination threshold of a biomimetic tactile sensor (BioTac®) that incorporates a similar, sensorized flat surface. We found the thresholds to be as small as 0.11° for tilts in the roll direction and 0.19° for tilts in the pitch direction. The flat surface was superior in detecting tilts when compared to other, curved locations on the BioTac.