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1. Contact Localization for Transparent Robots using Velocity Constraints

   Wang, Sean J.; Bhatia, Ankit; Mason, Matthew T.; Johnson, Aaron M. (January 2020, Proceedings of Dynamic Walking 2020)

   Robots operating in unstructured environments must localize contact to detect and recover from failure. For example, Fig. 1 shows a Minitaur robot that must localize where it has unexpectedly contacted the stair’s edge so that it can properly step over it. We propose a kinematic method for proprioceptive contact localization using velocity measurements. The method is validated on two planar robots, the quadrupedal Minitaur and the DD Hand gripper, and compared to other state of the art proprioceptive methods. We further show that the method can be extended to spatial robots by fusing the candidate contact points over time with a particle filter. 

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2. Strain-Based Consensus in Soft, Inflatable Robots

   https://doi.org/10.1109/RoboSoft54090.2022.9762180  

   Nilles, Alexandra; Ceron, Steven; Napp, Nils; Petersen, Kirstin (April 2022, IEEE Intl. Conference on Soft Robotics)

   Soft robots actuate themselves and their world through induced pressure and strain, and can often sense these quantities as well. We hypothesize that coordination in a tightly coupled collective of soft robots can be achieved with purely proprioceptive sensing and no direct communication. In this paper, we target a platform of soft pneumatic modules capable of sensing strain on their perimeter, with the goal of using only the robots' own soft actuators and sensors as a medium for distributed coordination. However, methods for modelling, sensing, and controlling strain in such soft robot collectives are not well understood. To address this challenge, we introduce and validate a computationally efficient spring-based model for two-dimensional sheets of soft pneumatic robots. We then translate a classical consensus algorithm to use only proprioceptive data, test in simulation, and show that due to the physical coupling between robots we can achieve consensus-like coordination. We discuss the unique challenges of strain sensors and next steps to bringing these findings to hardware. These findings have promising potential for smart materials and large-scale collectives, because they omit the need for additional communication infrastructure to support coordination. 

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3. Untethered Fluidic Engine for High‐Force Soft Wearable Robots

   https://doi.org/10.1002/aisy.202400171  

   Di_Lallo, Antonio; Yu, Shuangyue; Slightam, Jonathon E; Gu, Grace X; Yin, Jie; Su, Hao (November 2024, Advanced Intelligent Systems)

   Fluid‐driven artificial muscles exhibit a behavior similar to biological muscles which makes them attractive as soft actuators for wearable assistive robots. However, state‐of‐the‐art fluidic systems typically face challenges to meet the multifaceted needs of soft wearable robots. First, soft robots are usually constrained to tethered pressure sources or bulky configurations based on flow control valves for delivery and control of high assistive forces. Second, although some soft robots exhibit untethered operation, they are significantly limited to low force capabilities. Herein, an electrohydraulic actuation system that enables both untethered and high‐force soft wearable robots is presented. This solution is achieved through a twofold design approach. First, a simplified direct‐drive actuation paradigm composed of motor, gear‐pump, and hydraulic artificial muscle (HAM) is proposed, which allows for a compact and lightweight (1.6 kg) valveless design. Second, a fluidic engine composed of a high‐torque motor with a custom‐designed gear pump is created, which is capable of generating high pressure (up to 0.75 MPa) to drive the HAM in delivering high forces (580 N). Experimental results show that the developed fluidic engine significantly outperforms state‐of‐the‐art systems in mechanical efficiency and suggest opportunities for effective deployment in soft wearable robots for human assistance. 

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4. 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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5. Proprioceptive Invariant Robot State Estimation

   Lin, Tzu-Yuan; Li, Tingjun; Tong, Wenzhe; Ghaffari, Maani (November 2023, arXiv)

   This paper reports on developing a real-time invariant proprioceptive robot state estimation framework called DRIFT. A didactic introduction to invariant Kalman filtering is provided to make this cutting-edge symmetry-preserving approach accessible to a broader range of robotics applications. Furthermore, this work dives into the development of a proprioceptive state estimation framework for dead reckoning that only consumes data from an onboard inertial measurement unit and kinematics of the robot, with two optional modules, a contact estimator and a gyro filter for low-cost robots, enabling a significant capability on a variety of robotics platforms to track the robot's state over long trajectories in the absence of perceptual data. Extensive real-world experiments using a legged robot, an indoor wheeled robot, a field robot, and a full-size vehicle, as well as simulation results with a marine robot, are provided to understand the limits of DRIFT. 

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