In silico case studies of compliant robots: AMARSI deliverable 3.3

In the deliverable 3.2 we presented how the morphological computing ap- proach can significantly facilitate the control strategy in several scenarios, e.g. quadruped locomotion, bipedal locomotion and reaching. In particular, the Kitty experimental platform is an example of the use of morphological computation to allow quadruped locomotion. In this deliverable we continue with the simulation studies on the application of the different morphological computation strategies to control a robotic system

Reinforcement Learning of CPG-regulated Locomotion Controller for a Soft Snake Robot

Intelligent control of soft robots is challenging due to the nonlinear and difficult-to-model dynamics. One promising model-free approach for soft robot control is reinforcement learning (RL). However, model-free RL methods tend to be computationally expensive and data-inefficient and may not yield natural and smooth locomotion patterns for soft robots. In this work, we develop a bio-inspired design of a learning-based goal-tracking controller for a soft snake robot. The controller is composed of two modules: An RL module for learning goal-tracking behaviors given the unmodeled and stochastic dynamics of the robot, and a central pattern generator (CPG) with the Matsuoka oscillators for generating stable and diverse locomotion patterns. We theoretically investigate the maneuverability of Matsuoka CPG's oscillation bias, frequency, and amplitude for steering control, velocity control, and sim-to-real adaptation of the soft snake robot. Based on this analysis, we proposed a composition of RL and CPG modules such that the RL module regulates the tonic inputs to the CPG system given state feedback from the robot, and the output of the CPG module is then transformed into pressure inputs to pneumatic actuators of the soft snake robot. This design allows the RL agent to naturally learn to entrain the desired locomotion patterns determined by the CPG maneuverability. We validated the optimality and robustness of the control design in both simulation and real experiments, and performed extensive comparisons with state-of-art RL methods to demonstrate the benefit of our bio-inspired control design.Comment: 20 pages, 17 figures, 4 tables, in IEEE Transactions on Robotic

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation

An originally chaotic system can be controlled into various periodic dynamics. When it is implemented into a legged robot's locomotion control as a central pattern generator (CPG), sophisticated gait patterns arise so that the robot can perform various walking behaviors. However, such a single chaotic CPG controller has difficulties dealing with leg malfunction. Specifically, in the scenarios presented here, its movement permanently deviates from the desired trajectory. To address this problem, we extend the single chaotic CPG to multiple CPGs with learning. The learning mechanism is based on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs' oscillation frequencies so that the robot adapts its locomotion to deal with the malfunction. As a consequence, the trajectory produced by the multiple chaotic CPGs resembles the original trajectory far better than the one produced by only a single CPG. The performance of the system is evaluated first in a physical simulation of a quadruped as well as a hexapod robot and finally in a real six-legged walking machine called AMOSII. The experimental results presented here reveal that using multiple CPGs with learning is an effective approach for adaptive locomotion generation where, for instance, different body parts have to perform independent movements for malfunction compensation.Comment: 48 pages, 16 figures, Information Sciences 201

Simulation and robotics studies of salamander locomotion: Applying neurobiological principles to the control of locomotion in robots

This article presents a project that aims at understanding the neural circuitry controlling salamander locomotion, and developing an amphibious salamander-like robot capable of replicating its bimodal locomotion, namely swimming and terrestrial walking. The controllers of the robot are central pattern generator models inspired by the salamander's locomotion control network. The goal of the project is twofold: (1) to use robots as tools for gaining a better understanding of locomotion control in vertebrates and (2) to develop new robot and control technologies for developing agile and adaptive outdoor robots. The article has four parts. We first describe the motivations behind the project. We then present neuromechanical simulation studies of locomotion control in salamanders. This is followed by a description of the current stage of the robotic developments. We conclude the article with a discussion on the usefulness of robots in neuroscience research with a special focus on locomotion contro

Technical Report: A Contact-aware Feedback CPG System for Learning-based Locomotion Control in a Soft Snake Robot

Integrating contact-awareness into a soft snake robot and efficiently controlling its locomotion in response to contact information present significant challenges. This paper aims to solve contact-aware locomotion problem of a soft snake robot through developing bio-inspired contact-aware locomotion controllers. To provide effective contact information for the controllers, we develop a scale covered sensor structure mimicking natural snakes' \textit{scale sensilla}. In the design of control framework, our core contribution is the development of a novel sensory feedback mechanism of the Matsuoka central pattern generator (CPG) network. This mechanism allows the Matsuoka CPG system to work like a "spine cord" in the whole contact-aware control scheme, which simultaneously takes the stimuli including tonic input signals from the "brain" (a goal-tracking locomotion controller) and sensory feedback signals from the "reflex arc" (the contact reactive controller), and generate rhythmic signals to effectively actuate the soft snake robot to slither through densely allocated obstacles. In the design of the "reflex arc", we develop two types of reactive controllers -- 1) a reinforcement learning (RL) sensor regulator that learns to manipulate the sensory feedback inputs of the CPG system, and 2) a local reflexive sensor-CPG network that directly connects sensor readings and the CPG's feedback inputs in a special topology. These two reactive controllers respectively facilitate two different contact-aware locomotion control schemes. The two control schemes are tested and evaluated in the soft snake robot, showing promising performance in the contact-aware locomotion tasks. The experimental results also further verify the benefit of Matsuoka CPG system in bio-inspired robot controller design.Comment: 17 pages, 19 figure