Exoskeleton-covered soft finger with vision-based proprioception and tactile sensing

Soft robots offer significant advantages in adaptability, safety, and dexterity compared to conventional rigid-body robots. However, it is challenging to equip soft robots with accurate proprioception and tactile sensing due to their high flexibility and elasticity. In this work, we describe the development of a vision-based proprioceptive and tactile sensor for soft robots called GelFlex, which is inspired by previous GelSight sensing techniques. More specifically, we develop a novel exoskeleton-covered soft finger with embedded cameras and deep learning methods that enable high-resolution proprioceptive sensing and rich tactile sensing. To do so, we design features along the axial direction of the finger, which enable high-resolution proprioceptive sensing, and incorporate a reflective ink coating on the surface of the finger to enable rich tactile sensing. We design a highly underactuated exoskeleton with a tendon-driven mechanism to actuate the finger. Finally, we assemble 2 of the fingers together to form a robotic gripper and successfully perform a bar stock classification task, which requires both shape and tactile information. We train neural networks for proprioception and shape (box versus cylinder) classification using data from the embedded sensors. The proprioception CNN had over 99\% accuracy on our testing set (all six joint angles were within 1 degree of error) and had an average accumulative distance error of 0.77 mm during live testing, which is better than human finger proprioception. These proposed techniques offer soft robots the high-level ability to simultaneously perceive their proprioceptive state and peripheral environment, providing potential solutions for soft robots to solve everyday manipulation tasks. We believe the methods developed in this work can be widely applied to different designs and applications.Comment: Accepted to ICRA202

Scalable Tactile Sensing for an Omni-adaptive Soft Robot Finger

Robotic fingers made of soft material and compliant structures usually lead to superior adaptation when interacting with the unstructured physical environment. In this paper, we present an embedded sensing solution using optical fibers for an omni-adaptive soft robotic finger with exceptional adaptation in all directions. In particular, we managed to insert a pair of optical fibers inside the finger's structural cavity without interfering with its adaptive performance. The resultant integration is scalable as a versatile, low-cost, and moisture-proof solution for physically safe human-robot interaction. In addition, we experimented with our finger design for an object sorting task and identified sectional diameters of 94\% objects within the $\pm$6mm error and measured 80\% of the structural strains within $\pm$0.1mm/mm error. The proposed sensor design opens many doors in future applications of soft robotics for scalable and adaptive physical interactions in the unstructured environment.Comment: 8 pages, 6 figures, full-length version of a submission to IEEE RoboSoft 202

Imaging skins: stretchable and conformable on-organ beta particle detectors for radioguided surgery

While radioguided surgery (RGS) traditionally relied on detecting gamma rays, direct detection of beta particles could facilitate the detection of tumour margins intraoperatively by reducing radiation noise emanating from distant organs, thereby improving the signal-to-noise ratio of the imaging technique. In addition, most existing beta detectors do not offer surface sensing or imaging capabilities. Therefore, we explore the concept of a stretchable scintillator to detect beta-particles emitting radiotracers that would be directly deployed on the targeted organ. Such detectors, which we refer to as imaging skins, would work as indirect radiation detectors made of light-emitting agents and biocompatible stretchable material. Our vision is to detect scintillation using standard endoscopes routinely employed in minimally invasive surgery. Moreover, surgical robotic systems would ideally be used to apply the imaging skins, allowing for precise control of each component, thereby improving positioning and task repeatability. While still in the exploratory stages, this innovative approach has the potential to improve the detection of tumour margins during RGS by enabling real-time imaging, ultimately improving surgical outcomes

Sensing, Design Optimization, and Motion Planning for Agile Pneumatic Artificial Muscle-Driven Robots

Mechanical compliance in robotic systems facilitates safe human-robot interaction and improves robot adaptation to environmental uncertainty. Several promising compliant actuator technologies have emerged from the field of soft robotics, in particular the pneumatic artificial muscle—a soft, lightweight actuator that contracts under pressure. The pneumatic muscle's passive compliance eliminates the need for precise high-bandwidth actuator control to simulate mechanical impedance. However, the pneumatic muscle is limited in practical robot applications—particularly, without sacrificing robot agility—due to several key challenges: development of compatible soft sensors, translation of conventional high-level control and planning techniques to pneumatic muscle-driven systems, and limitations in pneumatic muscle pressurization rate and force generation capabilities. This work seeks to address these challenges, via a threefold approach, to access the benefits of compliant robot actuation while maximizing the robot's dynamic capabilities. The first objective targets the development of a pneumatic muscle design with integrated sensing to enable kinematic and dynamic state estimation of muscle-actuated robots without hindering muscle compliance. The second objective focuses on the construction of a trajectory optimization framework for planning dynamic robot maneuvers using 'burst-inflation' muscle pressure control. Finally, the third objective explores a design optimization strategy utilizing biological joint mechanisms to compensate for pneumatic muscle limitations and maximize robot agility.Ph.D

Data-driven Tactile Sensing using Spatially Overlapping Signals

Providing robots with distributed, robust and accurate tactile feedback is a fundamental problem in robotics because of the large number of tasks that require physical interaction with objects. Tactile sensors can provide robots with information about the location of each point of contact with the manipulated object, an estimation of the contact forces applied (normal and shear) and even slip detection. Despite significant advances in touch and force transduction, tactile sensing is still far from ubiquitous in robotic manipulation. Existing methods for building touch sensors have proven difficult to integrate into robot fingers due to multiple challenges, including difficulty in covering multicurved surfaces, high wire count, or packaging constrains preventing their use in dexterous hands. In this dissertation, we focus on the development of soft tactile systems that can be deployed over complex, three-dimensional surfaces with a low wire count and using easily accessible manufacturing methods. To this effect, we present a general methodology called spatially overlapping signals. The key idea behind our method is to embed multiple sensing terminals in a volume of soft material which can be deployed over arbitrary, non-developable surfaces. Unlike a traditional taxel, these sensing terminals are not capable of measuring strain on their own. Instead, we take measurements across pairs of sensing terminals. Applying strain in the receptive field of this terminal pair should measurably affect the signal associated with it. As we embed multiple sensing terminals in this soft material, a significant overlap of these receptive fields occurs across the whole active sensing area, providing us with a very rich dataset characterizing the contact event. The use of an all-pairs approach, where all possible combinations of sensing terminals pairs are used, maximizes the number of signals extracted while reducing the total number of wires for the overall sensor, which in turn facilitates its integration. Building an analytical model for how this rich signal set relates to various contacts events can be very challenging. Further, any such model would depend on knowing the exact locations of the terminals in the sensor, thus requiring very precise manufacturing. Instead, we build forward models of our sensors from data. We collect training data using a dataset of controlled indentations of known characteristics, directly learning the mapping between our signals and the variables characterizing a contact event. This approach allows for accessible, cheap manufacturing while enabling extensive coverage of curved surfaces. The concept of spatially overlapping signals can be realized using various transduction methods; we demonstrate sensors using piezoresistance, pressure transducers and optics. With piezoresistivity we measure resistance values across various electrodes embedded in a carbon nanotubes infused elastomer to determine the location of touch. Using commercially available pressure transducers embedded in various configurations inside a soft volume of rubber, we show its possible to localize contacts across a curved surface. Finally, using optics, we measure light transport between LEDs and photodiodes inside a clear elastomer which makes up our sensor. Our optical sensors are able to detect both the location and depth of an indentation very accurately on both planar and multicurved surfaces. Our Distributed Interleaved Signals for Contact via Optics or D.I.S.C.O Finger is the culmination of this methodology: a fully integrated, sensorized robot finger, with a low wire count and designed for easy integration into dexterous manipulators. Our DISCO Finger can generally determine contact location with sub-millimeter accuracy, and contact force to within 10% (and often with 5%) of the true value without the need for analytical models. While our data-driven method requires training data representative of the final operational conditions that the system will encounter, we show our finger can be robust to novel contact scenarios where the shape of the indenter has not been seen during training. Moreover, the forward model that predicts contact locations and applied normal force can be transfered to new fingers with minimal loss of performance, eliminating the need to collect training data for each individual finger. We believe that rich tactile information, in a highly functional form with limited blind spots and a simple integration path into complete systems, like we demonstrate in this dissertation, will prove to be an important enabler for data-driven complex robotic motor skills, such as dexterous manipulation