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1. Reuleaux Triangle–Based Two Degrees of Freedom Bipedal Robot

   https://doi.org/10.3390/robotics10040114  

   Yang, Jiteng; Saab, Wael; Liu, Yujiong; Ben-Tzvi, Pinhas (December 2021, Robotics)

   This paper presents the design, modeling, analysis, and experimental results of a novel bipedal robotic system that utilizes two interconnected single degree-of-freedom (DOF) leg mechanisms to produce stable forward locomotion and steering. The single DOF leg is actuated via a Reuleaux triangle cam-follower mechanism to produce a constant body height foot trajectory. Kinematic analysis and dimension selection of the Reuleaux triangle mechanism is conducted first to generate the desired step height and step length. Leg sequencing is then designed to allow the robot to maintain a constant body height and forward walking velocity. Dynamic simulations and experiments are conducted to evaluate the walking and steering performance. The results show that the robot is able to control its body orientation, maintain a constant body height, and achieve quasi-static locomotion stability. 

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2. Brain–body-task co-adaptation can improve autonomous learning and speed of bipedal walking

   https://doi.org/10.1088/1748-3190/ad8419  

   Urbina-Meléndez, Darío; Azadjou, Hesam; Valero-Cuevas, Francisco J (October 2024, Bioinspiration & Biomimetics)

   Abstract Inspired by animals that co-adapt their brain and body to interact with the environment, we present a tendon-driven and over-actuated (i.e.njoint,n+1 actuators) bipedal robot that (i) exploits its backdrivable mechanical properties to manage body-environment interactions without explicit control,and(ii) uses a simple 3-layer neural network to learn to walk after only 2 min of ‘natural’ motor babbling (i.e. an exploration strategy that is compatible with leg and task dynamics; akin to childsplay). This brain–body collaboration first learns to produce feet cyclical movements ‘in air’ and, without further tuning, can produce locomotion when the biped is lowered to be in slight contact with the ground. In contrast, training with 2 min of ‘naïve’ motor babbling (i.e. an exploration strategy that ignores leg task dynamics), does not produce consistent cyclical movements ‘in air’, and produces erratic movements and no locomotion when in slight contact with the ground. When further lowering the biped and making the desired leg trajectories reach 1 cm below ground (causing the desired-vs-obtained trajectories error to be unavoidable), cyclical movements based on either natural or naïve babbling presented almost equally persistent trends, and locomotion emerged with naïve babbling. Therefore, we show how continual learning of walking in unforeseen circumstances can be driven by continual physical adaptation rooted in the backdrivable properties of the plant and enhanced by exploration strategies that exploit plant dynamics. Our studies also demonstrate that the bio-inspired co-design and co-adaptations of limbs and control strategies can produce locomotion without explicit control of trajectory errors. 

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3. Multi-robot connection towards collective obstacle field traversal

   Hu, Haodi; Liao, Xingjue; Du, Wuhao; Qian, Feifei (September 2024, IEEE International Conference on Robotics and Automation (ICRA))

   Environments with large terrain height variations present great challenges for legged robot locomotion. Drawing inspiration from fire ants’ collective assembly behavior, we study strategies that can enable two “connectable” robots to collectively navigate over bumpy terrains with height variations larger than robot leg length. Each robot was designed to be extremely simple, with a cubical body and one rotary motor actuating four vertical peg legs that move in pairs. Two or more robots could physically connect to one another to enhance collective mobility. We performed locomotion experiments with a two-robot group, across an obstacle field filled with uniformlydistributed semi-spherical “boulders”. Experimentally-measured robot speed suggested that the connection length between the robots has a significant effect on collective mobility: connection length C ∈ [0.86, 0.9] robot unit body length (UBL) were able to produce sustainable movements across the obstacle field, whereas connection length C ∈ [0.63, 0.84] and [0.92, 1.1] UBL resulted in low traversability. An energy landscape based model revealed the underlying mechanism of how connection length modulated collective mobility through the system’s potential energy landscape, and informed adaptation strategies for the two-robot system to adapt their connection length for traversing obstacle fields with varying spatial frequencies. Our results demonstrated that by varying the connection configuration between the robots, the tworobot system could leverage mechanical intelligence to better utilize obstacle interaction forces and produce improved locomotion. Going forward, we envision that generalized principles of robotenvironment coupling can inform design and control strategies for a large group of small robots to achieve ant-like collective environment negotiation. 

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4. Implementation of Deep Deterministic Policy Gradients for Controlling Dynamic Bipedal Walking

   https://doi.org/10.1007/978-3-319-95972-6_29  

   Liu, Chujun; Lonsberry, Andrew G.; Nandor, Mark J.; Audu, Musa L.; Quinn, Roger D. (July 2018, Living Machines: Conference on Biomimetic and Biohybrid Systems)

   A control system for simulated two-dimensional bipedal walking was developed. The biped model was built based on anthropometric data. At the core of the control is a Deep Deterministic Policy Gradients (DDPG) neural network that is trained in GAZEBO, a physics simulator, to predict the ideal foot location to maintain stable walking under external impulse load. Additional controllers for hip joint movement during stance phase, and ankle joint torque during toeoff, help to stabilize the robot during walking. The simulated robot can walk at a steady pace of approximately 1m/s, and during locomotion it can maintain stability with a 30N-s impulse applied at the torso. This work implement DDPG algorithm to solve biped walking control problem. The complexity of DDPG network is decreased through carefully selected state variables and distributed control system. 

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5. Adaptive load feedback robustly signals force dynamics in robotic model of Carausius morosus stepping

   https://doi.org/10.3389/fnbot.2023.1125171  

   Zyhowski, William P.; Zill, Sasha N.; Szczecinski, Nicholas S. (January 2023, Frontiers in Neurorobotics)

   Animals utilize a number of neuronal systems to produce locomotion. One type of sensory organ that contributes in insects is the campaniform sensillum (CS) that measures the load on their legs. Groups of the receptors are found on high stress regions of the leg exoskeleton and they have significant effects in adapting walking behavior. Recording from these sensors in freely moving animals is limited by technical constraints. To better understand the load feedback signaled by CS to the nervous system, we have constructed a dynamically scaled robotic model of the Carausius morosus stick insect middle leg. The leg steps on a treadmill and supports weight during stance to simulate body weight. Strain gauges were mounted in the same positions and orientations as four key CS groups (Groups 3, 4, 6B, and 6A). Continuous data from the strain gauges were processed through a previously published dynamic computational model of CS discharge. Our experiments suggest that under different stepping conditions (e.g., changing “body” weight, phasic load stimuli, slipping foot), the CS sensory discharge robustly signals increases in force, such as at the beginning of stance, and decreases in force, such as at the end of stance or when the foot slips. Such signals would be crucial for an insect or robot to maintain intra- and inter-leg coordination while walking over extreme terrain. 

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