Research

We are experiencing rapid growth in the number and complexity of robots seen in our daily lives. In the future, robot assistants will be prevalent in public and in our homes, and they should be able to interact safely with humans and their surroundings. These interactive and collaborative robots should not rely only on visual and verbal cues, but they should also understand how people communicate through tactile information as it plays a subtle but crucial role in our daily interactions. Soft robotics is a rapidly growing research area because of its potential to be safe to physically interact with and robust in unstructured environments, like what is experienced in real-world environments. The intrinsically compliant nature of soft materials enables these robots to be appealing for applications including grasping, manipulation, touch, assistive devices, wearables, and physical human-robot interaction (pHRI). Informative touch for intelligent soft robots will combine soft robots and sensors, machine learning, and feedback control to build robots that can better interact with the world. My research goal is to seamlessly integrate soft robots and sensing, to develop algorithms for and close the loop on robots that can physically interact with their environment .

Informative soft robotic touch for physical human-robot interaction, as inspired by Baymax from Disney's Big Hero 6.

Electronic skins and machine learning for intelligent soft robots

Soft robots have garnered interest for real-world applications because of their intrinsic safety embedded at the material level. These robots use deformable materials capable of shape and behavioral changes and allow conformable physical contact for manipulation. Yet, with the introduction of soft and stretchable materials to robotic systems comes a myriad of challenges for sensor integration, including multimodal sensing capable of stretching, embedment of high-resolution but large-area sensor arrays, and sensor fusion with an increasing volume of data. This Review explores the emerging confluence of e-skins and machine learning, with a focus on how roboticists can combine recent developments from the two fields to build autonomous, deployable soft robots, integrated with capabilities for informative touch and proprioception to stand up to the challenges of real-world environments.

Related Publications:

Shih B., Shah D. S., Li J., Thuruthel T. G., Park Y.-L., Iida F., Bao Z., Kramer-Bottiglio R., Tolley M. T. (2020), “Electronic skins and machine learning for intelligent soft robots”, Science Robotics, 5:41, eaaz9239.

Classification of components of affective touch using rapidly-manufacturable soft sensor skins

Affective touches play an important role in ev- eryday communication because a significant amount of human interactions are through physical contacts. To facilitate robust, safe human-robot interactions (HRIs), we require robots with soft skins that can interpret affective touches. In this paper, we describe the fabrication of rapidly manufacturable, soft sensor skins using liquid metal embedded silicone elastomer as a resistive element and trained a recurrent neural network (RNN) to distinguish between a variety of pokes and rubs from human users. On a 2×2 sensor array, we obtained an average classification accuracy of 97% for ten different types of pokes and rubs, demonstrating that the combination of a soft sensor and machine learning can classify the interactions. Our approach is a step towards intelligent soft robots that can understand social interactions through touches.

Related Publications:

Shih, B., Lathrop, E., Adibnazari, I., Martin, R., Park, Y.-L., Tolley, M.T. (2020), "Classification of components of affective touch using rapidly manufacturable, soft, sensor skins", in 2020 IEEE-RAS International Conference on Soft Robotics (ROSO20).

Variable Stiffness Devices Using Fiber Jamming for Application in Soft Robotics and Wearable Haptics

Variable stiffness actuation has applications in a wide range of fields, including wearable haptics, soft robots, and minimally invasive surgical devices. There have been numerous design approaches to control and tune stiffness and rigidity; however, most have relatively low specific load-carrying capacities (especially for flexural loads) in the most rigid state that restricts their use in small or slender devices. In this article, we present an approach to the design of slender, high flexural stiffness modules based on the principle of fiber jamming. The proposed fiber jamming modules (FJMs) consist of axially packed fibers in an airtight envelope that transition from a flexible to a rigid beam when a vacuum is created inside the envelope. This FJM can provide the flexural stiffness of up to eight times that of a particle jamming module in the rigid state. Unlike layer jamming modules, the design of FJMs further allows them to control stiffness while bending in space. We present an analytical model to guide the parameter choices for the design of fiber jamming devices. Finally, we demonstrate applications of FJMs, including as a versatile tool, as part of a kinesthetic force feedback haptic glove and as a programmable structure.

Related Publications:

Jadhav, S., Majit, M. R. A., Shih, B., Schulze, J. P., & Tolley, M. T. (2021). "Variable stiffness devices using fiber jamming for application in soft robotics and wearable haptics". Soft Robotics, https://doi.org/10.1089/soro.2019.0203.

Morphing Structure for Changing Hydrodynamic Characteristics of a Soft Underwater Walking Robot

Existing platforms for underwater exploration and inspection are often limited to traversing open water and must expend large amounts of energy to maintain a position in flow for long periods of time. Many benthic animals overcome these limitations using legged locomotion and have different hydrodynamic profiles dictated by different body morphologies. This work presents an underwater legged robot with soft legs and a soft inflatable morphing body that can change shape to influence its hydrodynamic characteristics. Flow over the morphing body separates behind the trailing edge of the inflated shape, so whether the protrusion is at the front, center, or back of the robot influences the amount of drag and lift. When the legged robot (2.87 N underwater weight) needs to remain stationary in flow, an asymmetrically inflated body resists sliding by reducing lift on the body by 40% (from 0.52 N to 0.31 N) at the highest flow rate tested while only increasing drag by 5.5% (from 1.75 N to 1.85 N). When the legged robot needs to walk with flow, a large inflated body is pushed along by the flow, causing the robot to walk 16% faster than it would with an uninflated body. The body shape significantly affects the ability of the robot to walk against flow as it is able to walk against 0.09 m/s flow with the uninflated body, but is pushed backwards with a large inflated body. We demonstrate that the robot can detect changes in flow velocity with a commercial force sensor and respond by morphing into a hydrodynamically preferable shape.

Related Publications:

Ishida M., Drotman D., Shih B., Hermes M., Luhar M., and Tolley M. T. (2019), "Morphing structure for changing hydrodynamic characteristics of a soft robot walking underwater", IEEE Robotics and Automation Letters, 4 (4), 4163-4169.

Soft robot perception using embedded soft sensors and recurrent neural networks

Recent work has begun to explore the design of biologically inspired soft robots composed of soft, stretchable materials for applications including the handling of delicate materials and safe interaction with humans. However, the solid-state sensors traditionally used in robotics are unable to capture the high-dimensional deformations of soft systems. Embedded soft resistive sensors have the potential to address this challenge. However, both the soft sensors—and the encasing dynamical system—often exhibit nonlinear time-variant behavior, which makes them difficult to model. In addition, the problems of sensor design, placement, and fabrication require a great deal of human input and previous knowledge. Drawing inspiration from the human perceptive system, we created a synthetic analog. Our synthetic system builds models using a redundant and unstructured sensor topology embedded in a soft actuator, a vision-based motion capture system for ground truth, and a general machine learning approach. This allows us to model an unknown soft actuated system. We demonstrate that the proposed approach is able to model the kinematics of a soft continuum actuator in real time while being robust to sensor nonlinearities and drift. In addition, we show how the same system can estimate the applied forces while interacting with external objects. The role of action in perception is also presented. This approach enables the development of force and deformation models for soft robotic systems, which can be useful for a variety of applications, including human-robot interaction, soft orthotics, and wearable robotics.

Related Publications:

Thuruthel T. G.*, Shih B.*, Laschi C., Tolley M. T., (2019) "Soft robot perception using embedded soft sensors and recurrent neural networks", Science Robotics, 4(26), eaav1488. *equal contribution.

3D printed resistive soft sensors

Sensor design for soft robots is a challenging problem because of the wide range of design parameters (e.g. geometry, material, actuation type, etc.) critical to their function. While conventional rigid sensors work effectively for soft robotics in specific situations, sensors that are directly integrated into the bodies of soft robots could help improve both their exteroceptive and interoceptive capabilities. To address this challenge, we designed sensors that can be co-fabricated with soft robot bodies using commercial 3D printers, without additional modification. We describe an approach to the design and fabrication of compliant, resistive soft sensors using a Connex3 Objet350 multimaterial printer and investigated an analytical comparison to sensors of similar geometries. The sensors consist of layers of commercial photopolymers with varying conductivities. We characterized the conductivity of TangoPlus, TangoBlackPlus, VeroClear, and Support705 materials under various conditions and demonstrate applications in which we can take advantage of these embedded sensors.

Related Publications:

Shih B., Christianson C., Gillespie K., Lee S., Mayeda J., Huo Z., Tolley M. T. (2019) "Design considerations for 3D printed, soft, multimaterial resistive sensors for soft robotics", Frontiers in Robotics and AI, 6, 30.

Shih B., Mayeda J., Huo Z., Christianson C., and Tolley M. T., "3D printed resistive soft sensors," in 2018 IEEE-RAS International Conference on Soft Robotics (RoboSoft), pp 152-157, Apr 2018. *Best Poster Finalist*

Custom Soft Robotic Gripper Sensor Skins for Haptic Object Visualization

Tactile sensing is an important capability for robots that assist or interact with humans or fragile objects in uncertain environments. An ongoing challenge for soft robots has been incorporating sensors that can recognize complex motions. We present sensor skins that enable haptic object visualization when integrated on a soft robotic gripper that can twist an object. First, we investigate how the design of the actuator modules impact bend angle and motion. Each soft finger is molded using a silicone elastomer, and consists of three pneumatic chambers which can be inflated independently to achieve a range of complex motions. Three fingers are combined to form a soft robotic gripper. Then, we manufacture and attach modular, flexible sensory skins on each finger to measure deformation and contact. These sensor measurements are used in conjunction with an analytical model to construct 2D and 3D tactile object models. Our results are a step towards soft robot grippers capable of a complex range of motions and proprioception, which will help future robots better understand the environments with which they interact, and have the potential to increase physical safety in human-robot interaction.

Related Publication:

Shih B., Drotman D., Christianson C., Huo Z., White R., Christensen H. I., Tolley M. T., (2017) "Custom Soft Robotic Gripper Sensor Skins for Haptic Object Visualization", Int. Conf. on Intelligent Robots and Systems (IROS), Vancouver, Sept. 2017.

Differential Pressure Control of 3D Printed Soft Fluidic Actuators

Fluidically actuated soft robots show a great promise for operation in sensitive and unknown environments due to their intrinsic compliance. However, most previous designs use either flow control systems that are noisy, inefficient, sensitive to leaks, and cannot achieve differential pressure (i.e. can only apply either positive or negative pressures with respect to atmospheric), or closed volume control systems that are not adaptable and prohibitively expensive. In this work, we present a modular, low cost volume control system for differential pressure control of soft actuators. We use this system to actuate three-chamber 3D printed soft robotic modules. For this design, we demonstrated improved performance when using differential pressure actuation as compared to the use of only pressure or vacuum. Furthermore, we demonstrate a self-healing capability of the combined system by using vacuum to actuate ruptured modules which were no longer responsive to positive pressure.

Related Publication:

Kalisky T., Wang Y., Shih B., Drotman D., Jadhav S., Aronoff-Spencer E., and Tolley M T., (2017) "Differential Pressure Control of 3D Printed Soft Fluidic Actuators", Int. Conf. on Intelligent Robots and Systems (IROS), Vancouver, Sept. 2017.