Investigating Sensorimotor Control in Locomotion using Robots and Mathematical Models

Locomotion is a very diverse phenomenon that results from the interactions of a body and its environment and enables a body to move from one position to another. Underlying control principles rely among others on the generation of intrinsic body movements, adaptation and synchronization of those movements with the environment, and the generation of respective reaction forces that induce locomotion. We use mathematical and physical models, namely robots, to investigate how movement patterns emerge in a specific environment, and to what extent central and peripheral mechanisms contribute to movement generation. We explore insect walking, undulatory swimming and bimodal terrestrial and aquatic locomotion. We present relevant findings that explain the prevalence of tripod gaits for fast climbing based on the outcome of an optimization procedure. We also developed new control paradigms based on local sensory pressure feedback for anguilliform swimming, which include oscillator-free and decoupled control schemes, and a new design methodology to create physical models for locomotion investigation based on a salamander-like robot. The presented work includes additional relevant contributions to robotics, specifically a new fast dynamically stable walking gait for hexapedal robots and a decentralized scheme for highly modular control of lamprey-like undulatory swimming robots

Biorobotics: Using robots to emulate and investigate agile animal locomotion

The graceful and agile movements of animals are difficult to analyze and emulate because locomotion is the result of a complex interplay of many components: the central and peripheral nervous systems, the musculoskeletal system, and the environment. The goals of biorobotics are to take inspiration from biological principles to design robots that match the agility of animals, and to use robots as scientific tools to investigate animal adaptive behavior. Used as physical models, biorobots contribute to hypothesis testing in fields such as hydrodynamics, biomechanics, neuroscience, and prosthetics. Their use may contribute to the design of prosthetic devices that more closely take human locomotion principles into account

Locomotion of Low-DoF Multi-legged Robots

Multi-legged robots inspired by insects and other arthropods have unique advantages when compared with bipedal and quadrupedal robots. Their sprawled posture provides stability, and allows them to utilize low-DoF legs which are easier to build and control. With low-DoF legs and multiple contacts with the environment, low-DoF multi-legged robots are usually over constrained if no slipping is allowed. This makes them intrinsically different from the classic bipedal and quadrupedal robots which have high-DoF legs and fewer contacts with the environment. Here we study the unique characteristics of low-DoF multi-legged robots, in terms of design, mobility and modeling. One key observation we prove is that 1-DoF multi-legged robots must slip to be able to steer in the plane. Slipping with multiple contacts makes it difficult to model these robots and their locomotion. Therefore, instead of relying on models, our primary strategy has been careful experimental study. We designed and built our own customized robots which are easily reconfigurable to accommodate a variety of research requirements. In this dissertation we present two robot platforms, BigAnt and Multipod, which demonstrate our design and fabrication methods for low-cost rapidly fabricated modular robotic platforms. BigAnt is a hexapedal robot with 1-DoF legs, whose chassis is constructed from foam board and fiber tape, and costs less than 20 USD in total; Multipod is a highly modular multi-legged robot that can be easily assembled to have different numbers of 2-DoF legs (4 to 12 legs discussed here). We conducted a detailed analysis of steering, including proposing a formal definition of steering gaits grounded in geometric mechanics, and demonstrated the intrinsic difference between legged steering and wheeled steering. We designed gaits for walking, steering, undulating, stair climbing, turning in place, and more, and experimentally tested all these gaits on our robot platforms with detailed motion tracking. Through the theoretical analyses and the experimental tests, we proved that allowing slipping is beneficial for improving the steering in our robots. Where conventional modeling strategies struggle due to multi-contact slipping, we made a significant scientific discovery: that multi-legged locomotion with slipping is often geometric in the sense known from the study of low Reynolds number swimmers and non-holonomic wheeled snake robots which have continuous contact with the environment. We noted that motion can be geometric ``on average'', i.e. stride to stride, and can be truly instantaneously geometric. For each of these we developed a data-driven modeling approach that allowed us to analyze the degree to which a motion is geometric, and applied the analysis to BigAnt and Multipod. These models can also be used for robot motion planning. To explore the mechanism behind the geometric motion characteristics of these robots, we proposed a spring supported multi-legged model. We tested the simulation based on this model against experimental data for all the systems we studied: BigAnt, Multipod, Mechapod (a variant of 6-legged Multipod) and cockroaches. The model prediction results captures many key features of system velocity profiles, but still showed some systematic errors (which can be alleviated ad-hoc). Our work shows the promise of low-DoF multi-legged robots as a class of robotic platforms that are easy to build and simulate, and have many of the mobility advantages of legged systems without the difficulties in stability and control that appear in robots with four or fewer legs.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169985/1/danzhaoy_1.pd

Limping following limb loss increases locomotor stability

Although many arthropods have the ability to voluntarily lose limbs, how these animals rapidly adapt to such an extreme perturbation remains poorly understood. It is thought that moving with certain gaits can enable efficient, stable locomotion; however, switching gaits requires complex information flow between and coordination of an animal's limbs. We show here that upon losing two legs, spiders can switch to a novel, more statically stable gait, or use temporal adjustments without a gait change. The resulting gaits have higher overall static stability than the gaits that would be imposed by limb loss. By decreasing the time spent in a low-stability configuration—effectively “limping” over less stable phases of the stride—spiders increased the overall stability of the less statically-stable gait with no observable reduction in speed, as compared to the intact condition. Our results shed light on how voluntary limb loss could have persisted evolutionarily among many animals, and provide bioinspired solutions for robots when they break or lose limbs

Identifying Important Sensory Feedback for Learning Locomotion Skills

Robot motor skills can be learned through deep reinforcement learning (DRL) by neural networks as state-action mappings. While the selection of state observations is crucial, there has been a lack of quantitative analysis to date. Here, we present a systematic saliency analysis that quantitatively evaluates the relative importance of different feedback states for motor skills learned through DRL. Our approach can identify the most essential feedback states for locomotion skills, including balance recovery, trotting, bounding, pacing and galloping. By using only key states including joint positions, gravity vector, base linear and angular velocities, we demonstrate that a simulated quadruped robot can achieve robust performance in various test scenarios across these distinct skills. The benchmarks using task performance metrics show that locomotion skills learned with key states can achieve comparable performance to those with all states, and the task performance or learning success rate will drop significantly if key states are missing. This work provides quantitative insights into the relationship between state observations and specific types of motor skills, serving as a guideline for robot motor learning. The proposed method is applicable to differentiable state-action mapping, such as neural network based control policies, enabling the learning of a wide range of motor skills with minimal sensing dependencies

Control of Bio-Inspired Sprawling Posture Quadruped Robots with an Actuated Spine

Sprawling posture robots are characterized by upper limb segments protruding horizontally from the body, resulting in lower body height and wider support on the ground. Combined with an actuated segmented spine and tail, such morphology resembles that of salamanders or crocodiles. Although bio-inspired salamander-like robots with simple rotational limbs have been created, not much research has been done on kinematically redundant bio-mimetic robots that can closely replicate kinematics of sprawling animal gaits. Being bio-mimetic could allow a robot to have some of the locomotion skills observed in those animals, expanding its potential applications in challenging scenarios. At the same time, the robot could be used to answer questions about the animal's locomotion. This thesis is focused on developing locomotion controllers for such robots. Due to their high number of degrees of freedom (DoF), the control is based on solving the limb and spine inverse kinematics to properly coordinate different body parts. It is demonstrated how active use of a spine improves the robot's walking and turning performance. Further performance improvement across a variety of gaits is achieved by using model predictive control (MPC) methods to dictate the motion of the robot's center of mass (CoM). The locomotion controller is reused on an another robot (OroBOT) with similar morphology, designed to mimic the kinematics of a fossil belonging to Orobates, an extinct early tetrapod. Being capable of generating different gaits and quantitatively measuring their characteristics, OroBOT was used to find the most probable way the animal moved. This is useful because understanding locomotion of extinct vertebrates helps to conceptualize major transitions in their evolution. To tackle field applications, e.g. in disaster response missions, a new generation of field-oriented sprawling posture robots was built. The robustness of their initial crocodile-inspired design was tested in the animal's natural habitat (Uganda, Africa) and subsequently enhanced with additional sensors, cameras and computer. The improvements to the software framework involved a smartphone user interface visualizing the robot's state and camera feed to improve the ease of use for the operator. Using force sensors, the locomotion controller is expanded with a set of reflex control modules. It is demonstrated how these modules improve the robot's performance on rough and unstructured terrain. The robot's design and its low profile allow it to traverse low passages. To also tackle narrow passages like pipes, an unconventional crawling gait is explored. While using it, the robot lies on the ground and pushes against the pipe walls to move the body. To achieve such a task, several new control and estimation modules were developed. By exploring these problems, this thesis illustrates fruitful interactions that can take place between robotics, biology and paleontology