Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots

Millirobots are a promising robotic platform for many applications due to their small size and low manufacturing costs. Legged millirobots, in particular, can provide increased mobility in complex environments and improved scaling of obstacles. However, controlling these small, highly dynamic, and underactuated legged systems is difficult. Hand-engineered controllers can sometimes control these legged millirobots, but they have difficulties with dynamic maneuvers and complex terrains. We present an approach for controlling a real-world legged millirobot that is based on learned neural network models. Using less than 17 minutes of data, our method can learn a predictive model of the robot's dynamics that can enable effective gaits to be synthesized on the fly for following user-specified waypoints on a given terrain. Furthermore, by leveraging expressive, high-capacity neural network models, our approach allows for these predictions to be directly conditioned on camera images, endowing the robot with the ability to predict how different terrains might affect its dynamics. This enables sample-efficient and effective learning for locomotion of a dynamic legged millirobot on various terrains, including gravel, turf, carpet, and styrofoam. Experiment videos can be found at https://sites.google.com/view/imageconddy

Towards understanding of climbing, tip-over prevention and self-righting behaviors in Hexapoda

Die vorliegende Dissertation mit dem Titel “Towards understanding of climbing, tip-over prevention and self-righting behaviors in Hexapoda” untersucht in drei Studien exemplarisch, wie (i) Wüstenameisen ihre Beine einsetzen um An- und Abstiege zu überwinden, wie (ii) Wüsten- und Waldameisen ein Umkippen an steilen Anstiegen vermeiden, und wie sich (iii) Madagaskar-Fauchschaben, Amerikanische Großschaben und Blaberus discoidalis Audinet-Servill, 1839 aus Rückenlagen drehen und aufrichten. Neuartige biomechanischen Beschreibungen umfassen unter anderem: Impuls- und Kraftwirkungen einzelner Ameisenbeine auf den Untergrund beim Bergauf- und Bergabklettern, Kippmomente bei kletternden Ameisen, Energiegebirge-Modelle (energy landscapes) zur Quantifizierung der Körperform für die funktionelle Beschreibung des Umdrehens aus der Rückenlage

Characterization of Dynamic Behaviors in a Hexapod Robot

This paper investigates the relationship between energetic effi- ciency and the dynamical structure of a legged robot’s gait. We present an experimental data set collected from an untethered dynamic hexapod, EduBot [1] (a RHex-class [2] machine), operating in four distinct manually selected gaits. We study the robot’s single tripod stance dynamics of the robot which are identified by a purely jointspace-driven estimation method introduced in this paper. Our results establish a strong relationship between energetic efficiency (simultaneous reduction in power consumption and in- crease in speed) and the dynamical structure of an alternating tripod gait as measured by its fidelity to the SLIP mechanics—a dynamical pattern exhibit- ing characteristic exchanges of kinetic and spring-like potential energy [3]. We conclude that gaits that are dynamic in this manner give rise to better uti- lization of energy for the purposes of locomotion. This work is supported in part by the National Science Foundation (NSF) under a FIBR Award 0425878. Yasemin Ozkan Aydin is supported by International Research Fellowship Programme of the Scientific and Technological Research Council of Turkey (TUBITAK). For more information: Kod*La

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. They can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a walking robot is a challenging task. In this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a biomechanical walking robot. The turning information is transmitted as descending steering signals to the locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations as well as escaping from sharp corners or deadlocks. Using backbone joint control embedded in the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments

Data-Driven Methods to Build Robust Legged Robots

For robots to ever achieve signicant autonomy, they need to be able to mitigate performance loss due to uncertainty, typically from a novel environment or morphological variation of their bodies. Legged robots, with their complex dynamics, are particularly challenging to control with principled theory. Hybrid events, uncertainty, and high dimension are all confounding factors for direct analysis of models. On the other hand, direct data-driven methods have proven to be equally dicult to employ. The high dimension and mechanical complexity of legged robots have proven challenging for hardware-in-the-loop strategies to exploit without signicant eort by human operators. We advocate that we can exploit both perspectives by capitalizing on qualitative features of mathematical models applicable to legged robots, and use that knowledge to strongly inform data-driven methods. We show that the existence of these simple structures can greatly facilitate robust design of legged robots from a data-driven perspective. We begin by demonstrating that the factorial complexity of hybrid models can be elegantly resolved with computationally tractable algorithms, and establish that a novel form of distributed control is predicted. We then continue by demonstrating that a relaxed version of the famous templates and anchors hypothesis can be used to encode performance objectives in a highly redundant way, allowing robots that have suffered damage to autonomously compensate. We conclude with a deadbeat stabilization result that is quite general, and can be determined without equations of motion.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155053/1/gcouncil_1.pd

Non-inertial Undulatory Locomotion Across Scales

Locomotion is crucial to behaviors such as predator avoidance, foraging, and mating. In particular, undulatory locomotion is one of the most common forms of locomotion. From microscopic flagellates to swimming fish and slithering snakes, this form of locomotion is a remarkably robust self-propulsion strategy that allows a diversity of organisms to navigate myriad environments. While often thought of as exclusive to limbless organisms, a variety of locomotors possessing few to many appendages rely on waves of undulation for locomotion. In inertial regimes, organisms can leverage the forces generated by their body and the surrounding medium's inertia to enhance their locomotion (e.g., coast or glide). On the other hand, in non-inertial regimes self-propulsion is dominated by damping (viscous or frictional), and thus the ability for organisms to generate motion is dependent on the sequence of internal shape changes. In this thesis, we study a variety of undulating systems that locomote in highly damped regimes. We perform studies on systems ranging from zero to many appendages. Specifically, we focus on four distinct undulatory systems: 1) C. elegans, 2) quadriflagellate algae (bearing four flagella), 3) centipedes on terrestrial environments, and 4) centipedes on fluid environments. For each of these systems, we study how the coordination of their many degrees of freedom leads to specific locomotive behaviors. Further, we propose hypotheses for the observed behaviors in the context of each of these system's ecology.Ph.D

What is Robotics: Why Do We Need It and How Can We Get It?

Robotics is an emerging synthetic science concerned with programming work. Robot technologies are quickly advancing beyond the insights of the existing science. More secure intellectual foundations will be required to achieve better, more reliable and safer capabilities as their penetration into society deepens. Presently missing foundations include the identification of fundamental physical limits, the development of new dynamical systems theory and the invention of physically grounded programming languages. The new discipline needs a departmental home in the universities which it can justify both intellectually and by its capacity to attract new diverse populations inspired by the age old human fascination with robots. For more information: Kod*la

Kinetic energy fluctuation-driven locomotor transitions on potential energy landscapes of beam obstacle traversal and ground self-righting

Animals’ physical interaction with their environment, although often difficult, is effective and enables them to move robustly by using and transitioning between different modes such as running and climbing. Although robots exhibit some of these transitions, we lack a principled approach to generating and controlling them using effective physical interaction. Bridging this knowledge gap, in addition to advancing our understanding of animal locomotion, will improve robotic mobility. Recent studies of physical interaction with environment discovered that during beam obstacle traversal and ground self-righting, discoid cockroaches use and transition between diverse locomotor modes probabilistically and via multiple pathways. To traverse beams, the animal first pushes against them, but eventually pitches up due to beam restoring forces, following which it either pushes across beams (pitch mode) or rolls into the gap (roll mode). To self-right, the animal opens and pushes its wings against the ground, which pitches its body forward (metastable mode), and then rolls sideways (roll mode). Here, we seek to begin to explain these observations by integrating biological, robotic, and physics studies. We focus on pitch-to-roll and metastable-to-roll transitions of cockroaches during escape and emergency responses and feedforward-controlled robots. We discovered that across both systems, physical interaction is stochastic, with animals showing more variability. Animal and robot system states are strongly attracted to basins of their potential energy landscape, resulting in stereotyped locomotor modes. Locomotor transitions are probabilistic barrier-crossing transitions between landscape basins. Whereas the animal and robot traversed stiff beams via roll mode, they pushed across flimsy beams, suggesting that modes with easier physical interaction are more probable to occur (more favorable). Varying potential energy barriers by changing beam torsional stiffness (in the animal and robot) and kinetic energy fluctuation by changing body oscillation (in the robot) in both beam traversal and self-righting revealed that kinetic energy fluctuation comparable to the barrier facilitates probabilistic transition to the more favorable mode. Changing the system configuration (self-righting robot's wing opening) facilitates transitions by lowering the barrier. The animal's pitch-to-roll transition during beam traversal occurred even with insufficient kinetic energy fluctuation, suggesting that sensory feedback may be involved. These discoveries support the use of potential energy landscapes as a framework to understand locomotor transitions. Finally, we implemented methods for tracking and 3-D reconstruction of small animal locomotion in an existing terrain treadmill

Autonomous Behaviors With A Legged Robot

Over the last ten years, technological advancements in sensory, motor, and computational capabilities have made it a real possibility for a legged robotic platform to traverse a diverse set of terrains and execute a variety of tasks on its own, with little to no outside intervention. However, there are still several technical challenges to be addressed in order to reach complete autonomy, where such a platform operates as an independent entity that communicates and cooperates with other intelligent systems, including humans. A central limitation for reaching this ultimate goal is modeling the world in which the robot is operating, the tasks it needs to execute, the sensors it is equipped with, and its level of mobility, all in a unified setting. This thesis presents a simple approach resulting in control strategies that are backed by a suite of formal correctness guarantees. We showcase the virtues of this approach via implementation of two behaviors on a legged mobile platform, autonomous natural terrain ascent and indoor multi-flight stairwell ascent, where we report on an extensive set of experiments demonstrating their empirical success. Lastly, we explore how to deal with violations to these models, specifically the robot\u27s environment, where we present two possible extensions with potential performance improvements under such conditions