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1. Learning a Decentralized Multi-arm Motion Planner

   Ha, Huy ; Xu, Jingxi ; Song, Shuran ( November 2020 , Proceedings of the 2020 Conference on Robot Learning)

   We present a closed-loop multi-arm motion planner that is scalable and flexible with team size. Traditional multi-arm robotic systems have relied on centralized motion planners, whose run times often scale exponentially with team size, and thus, fail to handle dynamic environments with open-loop control. In this paper, we tackle this problem with multi-agent reinforcement learning, where a shared policy network is trained to control each individual robot arm to reach its target end-effector pose given observations of its workspace state and target end-effector pose. The policy is trained using Soft Actor-Critic with expert demonstrations from a sampling-based motion planning algorithm (i.e., BiRRT). By leveraging classical planning algorithms, we can improve the learning efficiency of the reinforcement learning algorithm while retaining the fast inference time of neural networks. The resulting policy scales sub-linearly and can be deployed on multi-arm systems with variable team sizes. Thanks to the closed-loop and decentralized formulation, our approach generalizes to 5-10 multiarm systems and dynamic moving targets (>90% success rate for a 10-arm system), despite being trained on only 1-4 arm planning tasks with static targets. 

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2. Harnessing Nonuniform Pressure Distributions in Soft Robotic Actuators

   https://doi.org/10.1002/aisy.202200330  

   Matia, Yoav ; Kaiser, Gregory H. ; Shepherd, Robert F. ; Gat, Amir D. ; Lazarus, Nathan ; Petersen, Kirstin H. ( January 2023 , Advanced Intelligent Systems)

   Herein, complex motion in soft, fluid‐driven actuators composed of elastomer bladders arranged around a neutral plane and connected by slender tubes is demonstrated. Rather than relying on complex feedback control or multiple inputs, the motion is generated with a single pressure input, leveraging viscous flows within the actuator to produce nonuniform pressure between bladders. Using an accurate predictive model coupling with a large deformation Cosserat rod model and low‐Reynolds‐number flow, all dominating dynamic interactions including extension and curvature are captured with two governing equations. Given insights from this model, five design elements are described and demonstrated in practice. By choosing the relative timescales between the solid, fluid, and input pressure cycles, the tip of the actuator can obtain almost any desired trajectory and can be placed anywhere temporarily within its 2D workspace. Finally, the benefits of viscous‐driven soft actuators are showcased in a six‐legged untethered robot able to walk 0.05 body lengths per second. The foundation is laid for a new class of morphologically intelligent, soft robotic actuators that enables complex deformations and multifunctionality without explicit drivers; whereby generating nonuniform pressure distributions, their infinite degrees of freedom can be exploited.

    

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3. Locomotion of a multi-link non-holonomic snake robot with passive joints

   Dear, T ; Buchanan, B ; Abrajan-Guerrero, R ; Kelly, SD ; Travers, M ; Choset, H ( April 2020 , The international journal of robotics research)

   Abstract Conventional approaches in prescribing controls for locomoting robots assume control over all input degrees of freedom (DOFs). Many robots, such as those with non-holonomic constraints, may not require or even allow for direct command over all DOFs. In particular, a snake robot with more than three links with non-holonomic constraints cannot achieve arbitrary configurations in all of its joints while simultaneously locomoting. For such a system, we assume partial command over a subset of the joints, and allow the rest to evolve according to kinematic chained and dynamic models. Different combinations of actuated and passive joints, as well as joints with dynamic elements such as torsional springs, can drastically change the coupling interactions and stable oscillations of joints. We use tools from nonlinear analysis to understand emergent oscillation modes of various robot configurations and connect them to overall locomotion using geometric mechanics and feedback control for robots that may not fully utilize all available inputs. We also experimentally verify observations and motion planning results on a physical non-holonomic snake robot 

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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. Synthetic Muscle Electroactive Polymer (EAP) pressure sensing and controlled shape-morphing for robotic grippers

   https://doi.org/10.1117/12.2583295  

   Briggs, Calum ; Cheng, Tianyu ; Meredith, Margot ; Vicars, Peter N. ; Rasmussen, Lenore ; Zhong, Alexander ( March 2021 , Proc. SPIE 11587, Electroactive Polymer Actuators and Devices (EAPAD) XXIII,)

   Madden, John D. ; Anderson, Iain A. ; Shea, Herbert R. (Ed.)

   Ras Labs makes Synthetic Muscle™, which is a class of electroactive polymer (EAP) based materials and actuators that sense pressure (gentle touch to high impact), controllably contract and expand at low voltage (1.5 V to 50 V, including use of batteries), and attenuate force. We are in the robotics era, but robots do have their challenges. Currently, robotic sensing is mainly visual, which is useful up until the point of contact. To understand how an object is being gripped, tactile feedback is needed. For handling fragile objects, if the grip is too tight, breakage occurs, and if the grip is too loose, the object will slip out of the grasp, also leading to breakage. Rigid robotic grippers using a visual feedback loop can struggle to determine the exact point and quality of contact. Robotic grippers can also get a stuttering effect in the visual feedback loop. By using soft Synthetic Muscle™ based EAP pads as the sensors, immediate feedback was generated at the first point of contact. Because these pads provided a soft, compliant interface, the first point of contact did not apply excessive force, allowing the force applied to the object to be controlled. The EAP sensor could also detect a change in pressure location on its surface, making it possible to detect and prevent slippage by then adjusting the grip strength. In other words, directional glide provided feedback for the presence of possible slippage to then be able to control a slightly tighter grip, without stutter, due to both the feedback and the soft gentleness of the fingertip-like EAP pads themselves. The soft nature of the EAP fingertip pad also naturally held the gripped object, improving the gripping quality over rigid grippers without an increase in applied force. Analogous to finger-like tactile touch, the EAPs with appropriate coatings and electronics were positioned as pressure sensors in the fingertip or end effector regions of robotic grippers. This development of using Synthetic Muscle™ based EAPs as soft sensors provided for sensors that feel like the pads of human fingertips. Basic pressure position and magnitude tests have been successful, with pressure sensitivity down to 0.05 N. Most automation and robots are very strong, very fast, and usually need to be partitioned away from humans for safety reasons. For many repetitive tasks that humans do with delicate or fragile objects, it would be beneficial to use robotics; whether it is for agriculture, medical surgery, therapeutic or personal care, or in extreme environments where humans cannot enter, including with contagions that have no cure. Synthetic Muscle™ was also retrofitted as actuator systems into off-the-shelf robotic grippers and is being considered in novel biomimetic gripper designs, operating at low voltages (less than 50 V). This offers biomimetic movement by contracting like human muscles, but also exceeds natural biological capabilities by expanding under reversed electric polarity. Human grasp is gentle yet firm, with tactile touch feedback. In conjunction with shape-morphing abilities, these EAPs also are being explored to intrinsically sense pressure due to the correlation between mechanical force applied to the EAP and its electronic signature. The robotic field is experiencing phenomenal growth in this fourth phase of the industrial revolution, the robotics era. The combination of Ras Labs’ EAP shape-morphing and sensing features promises the potential for robotic grippers with human hand-like control and tactile sensing. This work is expected to advance both robotics and prosthetics, particularly for collaborative robotics to allow humans and robots to intuitively work safely and effectively together. 

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