Download Free Structuring Optimal Control Of Legged Locomotion With Learning Based Methods Book in PDF and EPUB Free Download. You can read online Structuring Optimal Control Of Legged Locomotion With Learning Based Methods and write the review.

Both optimal control methods and learning-based methods have been widely used for the control of legged locomotion. While optimal control formulations allow the designer to guarantee constraints on the solutions found, learning-based methods can leverage data and past experiences to globally search for solutions robust to noise and errors in the model parameters. This work explores how optimal control methods can be guided and structured by using data-driven techniques such as supervised learning and Bayesian optimization. Two case studies are presented. The first presents a model predictive controller for quadrupedal landing that reasons about body states, reaction forces, and contact timings in an online fashion. This highly nonlinear problem is made tractable by collecting thousands of feasible trajectories offline from trajectory optimizations and learning to generate them from the initial falling conditions. By initializing the search for a solution with the approximation from a deep neural network, the MIT Mini Cheetah is shown to be able to recover from significant falls in simulation and hardware in real time. The second studies the effect of learning command-dependent weights for a convex model predictive controller. The weights for the running costs are adjusted dynamically as a function of the command input. Using black-box optimization techniques and a defined higher-level reward, a function mapping the command input to the weights can be determined by sampling the trajectories from sweeps of command inputs.
The development of legged robots capable of navigating in and interacting with the world is quickly advancing as new methods and techniques for sensing, decision-making, and controls expand the capabilities of state-of-the-art systems. Model-based methods, empowered by greater computing capacity and clever formulations, are imbuing systems with further physics-based understanding. While machine learning techniques, enabled by parallelized data generation and more efficient training, are imparting greater robustness to noise and abilities to handle poorly defined world features. Together these tools constitute the two major paradigms of legged robot research and while both have their shortcomings, they have complementary limitations that can be reinforced by the other's strengths. We propose MIMOC: Motion Imitation from Model-Based Optimal Control. MIMOC is a Reinforcement Learning (RL) locomotion controller that learns agile locomotion by imitating reference trajectories from model-based optimal control. MIMOC mitigates challenges faced by other motion imitation-based RL approaches because the generated reference trajectories are dynamically consistent, require no motion retargeting, and include torque references that are essential to learn dynamic locomotion. As a result, MIMOC does not require any fine-tuning to transfer the policy to the real robots. MIMOC also overcomes key issues with model-based optimal controllers. Since it is trained with simulated sensor noise and domain randomization, MIMOC is less sensitive to modeling and state estimation inaccuracies. We validate MIMOC on the Mini-Cheetah in outdoor environments over a wide variety of challenging terrain and on the MIT Humanoid in simulation. We show that MIMOC can transfer to the real-world and to different legged platforms. We also show cases where MIMOC outperforms model-based optimal controllers, and demonstrate the value of imitating torque references.
Online learning and controller adaptation will be an essential component for legged robots in the next few years as they begin to leave the laboratory setting and join our world. I present the first example of a learning system which is able to quickly and reliably acquire a robust feedback control policy for 3D dynamic bipedal walking from a blank slate using only trials implemented on the physical robot. The robot begins walking within a minute and learning converges in approximately 20 minutes. The learning works quickly enough that the robot is able to continually adapt to the terrain as it walks. This success can be attributed in part to the mechanics of our robot, which is capable of stable walking down a small ramp even when the computer is turned off. In this thesis, I analyze the dynamics of passive dynamic walking, starting with reduced planar models and working up to experiments on our real robot. I describe, in detail, the actor-critic reinforcement learning algorithm that is implemented on the return map dynamics of the biped. Finally, I address issues of scaling and controller augmentation using tools from optimal control theory and a simulation of a planar one-leg hopping robot. These learning results provide a starting point for the production of robust and energy efficient walking and running robots that work well initially, and continue to improve with experience.
Bioinspired Legged Locomotion: Models, Concepts, Control and Applications explores the universe of legged robots, bringing in perspectives from engineering, biology, motion science, and medicine to provide a comprehensive overview of the field. With comprehensive coverage, each chapter brings outlines, and an abstract, introduction, new developments, and a summary. Beginning with bio-inspired locomotion concepts, the book's editors present a thorough review of current literature that is followed by a more detailed view of bouncing, swinging, and balancing, the three fundamental sub functions of locomotion. This part is closed with a presentation of conceptual models for locomotion. Next, the book explores bio-inspired body design, discussing the concepts of motion control, stability, efficiency, and robustness. The morphology of legged robots follows this discussion, including biped and quadruped designs. Finally, a section on high-level control and applications discusses neuromuscular models, closing the book with examples of applications and discussions of performance, efficiency, and robustness. At the end, the editors share their perspective on the future directions of each area, presenting state-of-the-art knowledge on the subject using a structured and consistent approach that will help researchers in both academia and industry formulate a better understanding of bioinspired legged robotic locomotion and quickly apply the concepts in research or products. Presents state-of-the-art control approaches with biological relevance Provides a thorough understanding of the principles of organization of biological locomotion Teaches the organization of complex systems based on low-dimensional motion concepts/control Acts as a guideline reference for future robots/assistive devices with legged architecture Includes a selective bibliography on the most relevant published articles
This book addresses the need in the field for a comprehensive review of motion planning algorithms and hybrid control methodologies for complex legged robots. Introducing a multidisciplinary systems engineering approach for tackling many challenges posed by legged locomotion, the book provides engineering detail including hybrid models for planar and 3D legged robots, as well as hybrid control schemes for asymptotically stabilizing periodic orbits in these closed-loop systems. Complete with downloadable MATLAB code of the control algorithms and schemes used in the book, this book is an invaluable guide to the latest developments and future trends in dynamical legged locomotion.
The 9-volume set LNAI 14267-14275 constitutes the proceedings of the 16th International Conference on Intelligent Robotics and Applications, ICIRA 2023, which took place in Hangzhou, China, during July 5–7, 2023. The 413 papers included in these proceedings were carefully reviewed and selected from 630 submissions. They were organized in topical sections as follows: Part I: Human-Centric Technologies for Seamless Human-Robot Collaboration; Multimodal Collaborative Perception and Fusion; Intelligent Robot Perception in Unknown Environments; Vision-Based Human Robot Interaction and Application. Part II: Vision-Based Human Robot Interaction and Application; Reliable AI on Machine Human Reactions; Wearable Sensors and Robots; Wearable Robots for Assistance, Augmentation and Rehabilitation of Human Movements; Perception and Manipulation of Dexterous Hand for Humanoid Robot. Part III: Perception and Manipulation of Dexterous Hand for Humanoid Robot; Medical Imaging for Biomedical Robotics; Advanced Underwater Robot Technologies; Innovative Design and Performance Evaluation of Robot Mechanisms; Evaluation of Wearable Robots for Assistance and Rehabilitation; 3D Printing Soft Robots. Part IV: 3D Printing Soft Robots; Dielectric Elastomer Actuators for Soft Robotics; Human-like Locomotion and Manipulation; Pattern Recognition and Machine Learning for Smart Robots. Part V: Pattern Recognition and Machine Learning for Smart Robots; Robotic Tactile Sensation, Perception, and Applications; Advanced Sensing and Control Technology for Human-Robot Interaction; Knowledge-Based Robot Decision-Making and Manipulation; Design and Control of Legged Robots. Part VI: Design and Control of Legged Robots; Robots in Tunnelling and Underground Space; Robotic Machining of Complex Components; Clinically Oriented Design in Robotic Surgery and Rehabilitation; Visual and Visual-Tactile Perception for Robotics. Part VII: Visual and Visual-Tactile Perception for Robotics; Perception, Interaction, and Control of Wearable Robots; Marine Robotics and Applications; Multi-Robot Systems for Real World Applications; Physical and Neurological Human-Robot Interaction. Part VIII: Physical and Neurological Human-Robot Interaction; Advanced Motion Control Technologies for Mobile Robots; Intelligent Inspection Robotics; Robotics in Sustainable Manufacturing for Carbon Neutrality; Innovative Design and Performance Evaluation of Robot Mechanisms. Part IX: Innovative Design and Performance Evaluation of Robot Mechanisms; Cutting-Edge Research in Robotics.
Legged animals have explored more of the Earth's surface than any human designed vehicle. The agility, adaptability, and efficiency found in nature continues to inspire robotics researchers to develop efficient leg designs robust, stable and adaptable control strategies that can rapid changes in the environment. Understanding the dynamics of ground collision and contact is critical to advancing the state of the art of legged robotics and allowing legged robotics to narrow the performance gap with legged animals. Unfortunately modeling the dynamics of collision requires attention not just to whole cycle measures like the coefficient of restitution but also to the transient measures of slip and initiation of chatter. This thesis contributes to the model-based design and control of legged robots by developing compliant contact models for systems where the deformation of the contact bodies is small and the contact forces can be considered to act through a single point. A novel visco-plastic contact model is developed to represent collision dynamics during legged locomotion. The relationship between the model's damping parameter and the coefficient of restitution is formulated using the energetic coefficient which permits energy consistent formulation for collisions that are non-collinear and include slip reversal. Given experimental data of the position and force of the foot, the model parameter estimation is performed with an offline genetic algorithm and an online unscented Kalman filter. The effectiveness of the methods are demonstrated on one-dimensional collisions of a single mass and a mass spring damper system. The methods presented allow for a physics-based study of the effect of leg and foot compliance on the energy efficiency of legged locomotion and of locomotion controllers. An actuated, non conservative, continuous contact SLIP model is developed for greater analysis of dynamics of running. Methodologies for finding passive (and active) gait controllers are of great interest to robotics but for non-conservative models, there are no passively stable fixed points around which to build such controllers. Minimal heuristic controllers are generated for bouncing gait generation which allow for stable hopping in the presence of actuator and ground contact energy losses. Together with the online inverse model parameter estimation, the approach advances robotics toward realizing adaptive optimal efficiency locomotion based on terrain measurements.
Legged robots have the potential to be highly dynamic machines capable of outperforming humans and animals in executing locomotion tasks within dangerous and unstructured environments. Unfortunately, current control methods still lack the ability to move with the agility and robustness needed to traverse arbitrary terrains with the same grace and reliability as animals. This dissertation presents the successful implementation of a novel nonlinear optimization-based Regularized Predictive Control (RPC) framework that optimizes robot states, footstep locations, and ground reaction forces over a future prediction horizon. RPC exploits expertly designed and data-driven extracted heuristics by directly embedding them in the optimization through regularization in the cost function. Well-designed regularization should bias results towards a "good enough" heuristic solution by shaping the cost space favorably, while allowing the optimization to find a better result if it exists. However, designing meaningful regularized cost functions and adequate heuristics is challenging and not straightforward. A novel framework is presented for automatically extracting and designing new principled legged locomotion heuristics by fitting simple intuitive models to simulated and experimental data using RPC. Statistically correlated relationships between desired commands, robot states, and optimal control inputs are found by allowing the optimization to more exhaustively search the cost space during offline explorations when not subjected to real-time computation constraints. This method extracts simple, but powerful heuristics that can approximate complex dynamics and account for errors stemming from model simplifications or parameter uncertainty without the loss of physical intuition. Nonlinear optimization-based controllers have shown improved capabilities in simulation, but fall short when implemented on hardware systems that must adhere to real-time computation constraints and physical limits. Various methods and algorithms critical to the success of the robot were developed to overcome these challenges. The controller is verified experimentally using the MIT Cheetah 3 and Mini Cheetah robot platforms. Results demonstrate the ability of the robot to track dynamic velocity and turn rate commands with a variety of parametrized gaits, remain upright through large impulsive and sustained disturbances, and traverse highly irregular terrains. All of these behaviors are achieved with no modifications to the controller structure and with one set of gains signifying the generalized robustness of RPC. This work represents a step towards more robust dynamic locomotion capabilities for legged robots.
The CISM-IFToMM RoManSy Symposia have played a dynamic role in the development of the theory and practice of robotics. The proceedings of the eleven symposia to date present a world view of the state of the art. The proceedings of this eleventh edition focus mainly on problems of mechanical engineering and control.