Proprioceptive Learning with Soft Polyhedral Networks

Proprioception is the "sixth sense" that detects limb postures with motor neurons. It requires a natural integration between the musculoskeletal systems and sensory receptors, which is challenging among modern robots that aim for lightweight, adaptive, and sensitive designs at a low cost. Here, we present the Soft Polyhedral Network with an embedded vision for physical interactions, capable of adaptive kinesthesia and viscoelastic proprioception by learning kinetic features. This design enables passive adaptations to omni-directional interactions, visually captured by a miniature high-speed motion tracking system embedded inside for proprioceptive learning. The results show that the soft network can infer real-time 6D forces and torques with accuracies of 0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also incorporate viscoelasticity in proprioception during static adaptation by adding a creep and relaxation modifier to refine the predicted results. The proposed soft network combines simplicity in design, omni-adaptation, and proprioceptive sensing with high accuracy, making it a versatile solution for robotics at a low cost with more than 1 million use cycles for tasks such as sensitive and competitive grasping, and touch-based geometry reconstruction. This study offers new insights into vision-based proprioception for soft robots in adaptive grasping, soft manipulation, and human-robot interaction.Comment: 20 pages, 10 figures, 2 tables, submitted to the International Journal of Robotics Research for revie

On the Use of Large Area Tactile Feedback for Contact Data Processing and Robot Control

The progress in microelectronics and embedded systems has recently enabled the realization of devices for robots functionally similar to the human skin, providing large area tactile feedback over the whole robot body. The availability of such kind of systems, commonly referred to as extit{robot skins}, makes possible to measure the contact pressure distribution applied on the robot body over an arbitrary area. Large area tactile systems open new scenarios on contact processing, both for control and cognitive level processing, enabling the interpretation of physical contacts. The contents proposed in this thesis address these topics by proposing techniques exploiting large area tactile feedback for: (i) contact data processing and classification; (ii) robot control

NeatSkin:A Discrete Impedance Tomography Skin Sensor

In this paper we present NeatSkin, a novel artificial skin sensor based on electrical impedance tomography. The key feature is a discrete network of fluidic channels which is used to infer the location of touch. Change in resistance of the conductive fluid within these channels during deformation is used to construct sensitivity maps. We present a method to simulate touch using this unique network-based, low output dimensionality approach. The efficacy is demonstrated by fabricating a NeatSkin sensor. This paves the way for the development of more complex channel networks and a higher resolution soft skin sensor with potential applications in soft robotics, wearable devices and safe human-robot interaction.</p

Scalable Tactile Sensing E-Skins Through Spatial Frequency Encoding

Most state-of-the-art tactile sensing arrays are not scalable to large numbers of sensing units due to their raster-scanned readout. This readout scheme results in a high degree of wiring complexity and a tradeoff between spatial and temporal resolution. In this thesis I present the use of spatial frequency encoding to develop asynchronous tactile sensor arrays with single-wire sensor transduction, no per-taxel electronics, and no scanning latency. I demonstrate this through two prototype devices, Neuroskin 1, which is developed using fabric-based e-textile materials, and Neuroskin 2, which is developed using fPCB. Like human skin, Neuroskin has a temporal resolution of 1 kHz and innate data compression where tactile data from an MxN Neuroskin is compressed into M+N values. Neuroskin 2 requires only four interface wires (regardless of its number of sensors) and can be easily scaled up through its development as an fPCB. To demonstrate the utility of the prototypes, Neuroskin was mounted onto a biomimetic robotic finger to palpate different textures and perform a texture discrimination task. Neuroskin 1 and 2 achieved 87% and 76% classification accuracy respectively in the texture discrimination task. Overall, the method of spatial-frequency encoding is theoretically scalable to support sensor arrays with thousands of sensing elements without latency, and the resolution of a Neuroskin array is only limited by the ADC sampling rate. Future tactile sensing systems can utilize the spatial frequency encoding architecture presented here to be dense, numerous, and flexible while retaining excellent temporal resolution

Design of a Low-cost Tactile Robotic Sleeve for Autonomous Endoscopes and Catheters

Recent developments in medical robotics have been significant, supporting the minimally invasive operation requirements, such as smaller devices and more feedback available to surgeons. Nevertheless, the tactile feedback from a catheter or endoscopic type robotic device has been restricted mostly on the tip of the device and was not aimed to support the autonomous movement of the medical device during operation. In this work, we design a robotic sheath/sleeve with a novel and more comprehensive approach, which can function for whole-body or segment-based feedback control as well as diagnostic purposes. The robotic sleeve has several types of piezo-resistive pressure and extension sensors, which are embedded at several latitudes and depths of the silicone substrate. The sleeve takes the human skin as a biological model for its structure. It has a better tactile sensation of the inner tissues in the torturous narrow channels such as cardiovascular or endo-luminal tracts in human body thus can be used to diagnose abnormalities. In addition to this capability, using the stretch sensors distributed alongside its body, the robotic sheath/sleeve can perceive the ego-motion of the robotic backbone of the catheter and can act as a position feedback device. Because of the silicone substrate, the sleeve contributes toward safety of the medical device passively by providing a compliant interface. As an active safety measure, the robotic sheath can sense blood-clots or sudden turns inside a channel and by modifying the local trajectory, and can prevent embolisms or tissue rupture. In the future, advanced manufacturing techniques will increase the capabilities of the tactile robotic sleeve