Optimizing Robotic Single Legged Locomotion with Reinforcement Learning
نویسندگان
چکیده
Humans and animals show a remarkable level of proficiency in their ways of locomotion. They exploit the dynamics of the whole body to perform a variety of motions such as jumping and running. The nature of legged robots raises big challenges for controlling these systems. High degrees of freedom (DOF) and highly nonlinear non-smooth dynamics (due to interaction with the environment) count amongst these difficulties. Our goal is to perform highly dynamic jumping and hopping maneuvers with a robotic leg while optimally exploiting the capabilities of the hardware in terms of maximum jump height, jump distance, and energy efficiency. We build on our platform ScarlETH [1], an articulated robotic leg which is electrically driven in the hip and knee joints by highly compliant Series Elastic Actuators (SEA) [2].
منابع مشابه
Reinforcement Learning of Robotic Legged Locomotion
Humans and animals show a remarkable level of proficiency in their ways of locomotion. They exploit the dynamics of the whole body to perform a variety of motions such as jumping and running. Hereby, the elasticity in the muscles and tendons carries a key role in enabling robust, dynamic and energy efficient locomotion [1]. At the Autonomous Systems Lab, we have developed the robotic leg ScarlE...
متن کاملAdaptive posture control of a four-legged walking machine using some principles of mammalian locomotion
This paper presents an adaptive control scheme for the four legged walking machine BISAM. The task of the adaptive control is to learn sensor based reflexes for posture control. For this purpose, an incremental learning scheme is developed based on reinforcement learning. For the planned trajectory of the CoM the data taken from a goat are chosen as a basis, to investigate the transfer potentia...
متن کاملA cerebellar approach to adaptive locomotion for legged robots
This paper describes a neural learning architecture for control of legged robots inspired by mammalian neurophysiology. Biological studies indicate that the cerebel-lum is a key part of an adaptive control system which enables mammals to display remarkable limb coordination during loco-motion. We present a distributed control system using reinforcement learning methods and mechanisms inspired b...
متن کاملOne-Legged Locomotion with a Compliant Passive Joint
Abstract. There is an increasing attention of exploiting compliant materials for the purpose of legged locomotion, because they provide significant advantages in locomotion performance with respect to energy efficiency and stability. Toward establishing a fundamental basis for this line of research, a minimalistic locomotion model of a single legged system is explored in this paper. By analyzin...
متن کاملImproved Dynamic Stability Using Reinforcement Learning
Many researchers studying legged locomotion have applied tools from reinforcement learning/optimal control to minimize characteristics of a walking gait, most notably the energy consumption. In this paper, we use similar tools to maximize the region of stability of the controller defined as the set of initial conditions from which the robot maintains its balance for at least 5 seconds. Experime...
متن کامل