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1. Optical Proximity Sensing for Pose Estimation During In-Hand Manipulation

   https://doi.org/10.1109/IROS47612.2022.9981692  

   Lancaster, Patrick; Gyawali, Pratik; Mavrogiannis, Christoforos; Srinivasa, Siddhartha S.; Smith, Joshua R. (October 2022, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   During in-hand manipulation, robots must be able to continuously estimate the pose of the object in order to generate appropriate control actions. The performance of algorithms for pose estimation hinges on the robot's sensors being able to detect discriminative geometric object features, but previous sensing modalities are unable to make such measurements robustly. The robot's fingers can occlude the view of environment- or robot-mounted image sensors, and tactile sensors can only measure at the local areas of contact. Motivated by fingertip-embedded proximity sensors' robustness to occlusion and ability to measure beyond the local areas of contact, we present the first evaluation of proximity sensor based pose estimation for in-hand manipulation. We develop a novel two-fingered hand with fingertip-embedded optical time-of-flight proximity sensors as a testbed for pose estimation during planar in-hand manipulation. Here, the in-hand manipulation task consists of the robot moving a cylindrical object from one end of its workspace to the other. We demonstrate, with statistical significance, that proximity-sensor based pose estimation via particle filtering during in-hand manipulation: a) exhibits 50% lower average pose error than a tactile-sensor based baseline; b) empowers a model predictive controller to achieve 30% lower final positioning error compared to when using tactile-sensor based pose estimates. 

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2. Soft Tubular Strain Sensors for Contact Detection

   https://doi.org/10.1007/978-3-031-38857-6_16  

   Dai, Kevin; Elangovan, Abirami; Whirley, Karen; Webster-Wood, Victoria A (August 2023, Springer, Cham.)

   Sensing and actuation are intricately connected in soft robotics, where contact may change actuator mechanics and robot behavior. To improve soft robotic control and performance, proprioception and contact sensors are needed to report robot state without altering actuation mechanics or introducing bulky, rigid components. For bioinspired McKibben-style fluidic actuators, prior work in sensing has focused on sensing the strain of the actuator by embedding sensors in the actuator bladder during fabrication, or by adhering sensors to the actuator surface after fabrication. However, material property mismatches between sensors and actuators can impede actuator performance, and many soft sensors available for use with fluidic actuators rely on costly or labor-intensive fabrication methods. Here, we demonstrate a low-cost and easy-to manufacture-tubular liquid metal strain sensor for use with soft actuators that can be used to detect actuator strain and contact between the actuator and external objects. The sensor is flexible, can be fabricated with commercial-off-the-shelf components, and can be easily integrated with existing soft actuators to supplement sensing, regardless of actuator shape or size. Furthermore, the soft tubular strain sensor exhibits low hysteresis and high sensitivity. The approach presented in this work provides a low-cost, soft sensing solution for broad application in soft robotics. 

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3. Distributed Sensor Networks Deployed Using Soft Growing Robots

   https://doi.org/10.1109/RoboSoft51838.2021.9479345  

   Gruebele, Alexander M.; Zerbe, Andrew C.; Coad, Margaret M.; Okamura, Allison M.; Cutkosky, Mark R. (January 2021, International Conference on Soft Robotics (RoboSoft))

   null (Ed.)

   Due to their ability to move without sliding relative to their environment, soft growing robots are attractive for deploying distributed sensor networks in confined spaces. Sensing of the state of such robots would add to their capabilities as human-safe, adaptable manipulators. However, incorporation of distributed sensors onto soft growing robots is challenging because it requires an interface between stiff and soft materials, and the sensor network needs to undergo significant strain. In this work, we present a method for adding sensors to soft growing robots that uses flexible printed circuit boards with self-contained units of microcontrollers and sensors encased in a laminate armor that protects them from unsafe curvatures. We demonstrate the ability of this system to relay directional temperature and humidity information in hard-to-access spaces. We also demonstrate and characterize a method for sensing the growing robot shape using inertial measurement units deployed along its length, and develop a mathematical model to predict its accuracy. This work advances the capabilities of soft growing robots, as well as the field of soft robot sensing. 

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4. Improving the Accuracy of Wearable Sensors for Human Locomotion Tracking Using Phase-Locked Regression Models

   https://doi.org/10.1109/ICORR.2019.8779428  

   Duong, Ton T.; Zhang, Huanghe; Lynch, T. Sean; Zanotto, Damiano (June 2019, 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR))

   The trend toward soft wearable robotic systems creates a compelling need for new and reliable sensor systems that do not require a rigid mounting frame. Despite the growing use of inertial measurement units (IMUs) in motion tracking applications, sensor drift and IMU-to-segment misalignment still represent major problems in applications requiring high accuracy. This paper proposes a novel 2-step calibration method which takes advantage of the periodic nature of human locomotion to improve the accuracy of wearable inertial sensors in measuring lower-limb joint angles. Specifically, the method was applied to the determination of the hip joint angles during walking tasks. The accuracy and precision of the calibration method were accessed in a group of N = 8 subjects who walked with a custom-designed inertial motion capture system at 85% and 115% of their comfortable pace, using an optical motion capture system as reference. In light of its low computational complexity and good accuracy, the proposed approach shows promise for embedded applications, including closed-loop control of soft wearable robotic systems. 

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5. Probabilistic Differentiable Filters Enable Ubiquitous Robot Control with Smartwatches

   Fabian C Weigend, Xiao Liu (September 2023, OpenReview)

   Ubiquitous robot control and human-robot collaboration using smart devices poses a challenging problem primarily due to strict accuracy requirements and sparse information. This paper presents a novel approach that incorporates a probabilistic differentiable filter, specifically the Differentiable Ensemble Kalman Filter (DEnKF), to facilitate robot control solely using Inertial Measurement Units (IMUs) from a smartwatch and a smartphone. The implemented system is cost-effective and achieves accurate estimation of the human pose state. Experiment results from human-robot handover tasks underscore that smart devices allow versatile and ubiquitous robot control. 

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