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1. Controlling the fold: proprioceptive feedback in a soft origami robot

   https://doi.org/10.3389/frobt.2024.1396082  

   Hanson, Nathaniel; Mensah, Immanuel Ampomah; Roberts, Sonia F; Healey, Jessica; Wu, Celina; Dorsey, Kristen L (May 2024, Frontiers in Robotics and AI)

   Kyriakopoulos, Kostas J; Polygerinos, Panagiotis (Ed.)

   We demonstrate proprioceptive feedback control of a one degree of freedom soft, pneumatically actuated origami robot and an assembly of two robots into a two degree of freedom system. The base unit of the robot is a 41 mm long, 3-D printed Kresling-inspired structure with six sets of sidewall folds and one degree of freedom. Pneumatic actuation, provided by negative fluidic pressure, causes the robot to contract. Capacitive sensors patterned onto the robot provide position estimation and serve as input to a feedback controller. Using a finite element approach, the electrode shapes are optimized for sensitivity at larger (more obtuse) fold angles to improve control across the actuation range. We demonstrate stable position control through discrete-time proportional-integral-derivative (PID) control on a single unit Kresling robot via a series of static set points to 17 mm, dynamic set point stepping, and sinusoidal signal following, with error under 3 mm up to 10 mm contraction. We also demonstrate a two-unit Kresling robot with two degree of freedom extension and rotation control, which has error of 1.7 mm and 6.1°. This work contributes optimized capacitive electrode design and the demonstration of closed-loop feedback position control without visual tracking as an input. This approach to capacitance sensing and modeling constitutes a major step towards proprioceptive state estimation and feedback control in soft origami robotics. 

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2. Distributed Proprioception of 3D Configuration in Soft, Sensorized Robots via Deep Learning

   Ryan L. Truby, Cosimo Della (April 2020, IEEE robotics automation letters)

   Creating soft robots with sophisticated, autonomous capabilities requires these systems to possess reliable, on-line proprioception of 3D configuration through integrated soft sensors. We present a framework for predicting a soft robot’s 3D configuration via deep learning using feedback from a soft, proprioceptive sensor skin. Our framework introduces a kirigami-enabled strategy for rapidly sensorizing soft robots using off-the-shelf materials, a general kinematic description for soft robot geometry, and an investigation of neural network designs for predicting soft robot configuration. Even with hysteretic, non-monotonic feedback from the piezoresistive sensors, recurrent neural networks show potential for predicting our new kinematic parameters and, thus, the robot’s configuration. One trained neural network closely predicts steady-state configuration during operation, though complete dynamic behavior is not fully captured. We validate our methods on a trunk-like arm with 12 discrete actuators and 12 proprioceptive sensors. As an essential advance in soft robotic perception, we anticipate our framework will open new avenues towards closed loop control in soft robotics. 

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3. Robot-Centric Perception of Human Groups

   https://doi.org/10.1145/3375798  

   Taylor, Angelique; Chan, Darren M.; Riek, Laurel D. (July 2020, ACM Transactions on Human-Robot Interaction)

   null (Ed.)

   The robotics community continually strives to create robots that are deployable in real-world environments. Often, robots are expected to interact with human groups. To achieve this goal, we introduce a new method, the Robot-Centric Group Estimation Model (RoboGEM), which enables robots to detect groups of people. Much of the work reported in the literature focuses on dyadic interactions, leaving a gap in our understanding of how to build robots that can effectively team with larger groups of people. Moreover, many current methods rely on exocentric vision, where cameras and sensors are placed externally in the environment, rather than onboard the robot. Consequently, these methods are impractical for robots in unstructured, human-centric environments, which are novel and unpredictable. Furthermore, the majority of work on group perception is supervised, which can inhibit performance in real-world settings. RoboGEM addresses these gaps by being able to predict social groups solely from an egocentric perspective using color and depth (RGB-D) data. To achieve group predictions, RoboGEM leverages joint motion and proximity estimations. We evaluated RoboGEM against a challenging, egocentric, real-world dataset where both pedestrians and the robot are in motion simultaneously, and show RoboGEM outperformed two state-of-the-art supervised methods in detection accuracy by up to 30%, with a lower miss rate. Our work will be helpful to the robotics community, and serve as a milestone to building unsupervised systems that will enable robots to work with human groups in real-world environments. 

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4. Synthesis of Control Barrier Functions Using a Supervised Machine Learning Approach

   https://doi.org/10.1109/IROS45743.2020.9341190  

   Srinivasan, Mohit; Dabholkar, Amogh; Coogan, Samuel; Vela, Patricio A. (October 2020, International Conference on Intelligent Robots and Systems)

   Control barrier functions are mathematical constructs used to guarantee safety for robotic systems. When integrated as constraints in a quadratic programming optimization problem, instantaneous control synthesis with real-time performance demands can be achieved for robotics applications. Prevailing use has assumed full knowledge of the safety barrier functions, however there are cases where the safe regions must be estimated online from sensor measurements. In these cases, the corresponding barrier function must be synthesized online. This paper describes a learning framework for estimating control barrier functions from sensor data. Doing so affords system operation in unknown state space regions without compromising safety. Here, a support vector machine classifier provides the barrier function specification as determined by sets of safe and unsafe states obtained from sensor measurements. Theoretical safety guarantees are provided. Experimental ROS-based simulation results for an omnidirectional robot equipped with LiDAR demonstrate safe operation. 

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5. Incipient Immobilization Detection for Lightweight Rovers Operating in Deformable Terrain

   https://doi.org/10.1115/1.4056408  

   Lines, Austin; Elliott, Joshua; Ray, Laura R. (December 2022, Journal of Autonomous Vehicles and Systems)

   Abstract This paper presents a new method of detecting incipient immobilization for a wheeled mobile robot operating in deformable terrain with high spatial variability. This approach uses proprioceptive sensor data from a four-wheeled, rigid chassis rover operating in poorly bonded, compressible snow to develop canonic, dynamical system models of the robot's operation. These serve as hypotheses in a multiple model estimation algorithm used to predict the robot's mobility in real-time. This prediction method eliminates the need for choosing an empirical wheel-terrain interaction model, determining terramechanics parameter values, or for collecting large training datasets needed for machine learning classification. When tested on field data, this new method warns of decreased mobility an average of 1.8 meters and 2.9 seconds before the rover is completely immobilized. This system also proves to be a reliable predictor of immobilization when evaluated in simulated scenarios of rovers with passive suspension maneuvering in more variable terrain. 

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