Reinforcement Learning of Single Legged Locomotion

This paper presents the application of reinforcement learning to improve the performance of highly dynamic single legged locomotion with compliant series elastic actuators. The goal is to optimally exploit the capabilities of the hardware in terms of maximum jump height, jump distance, and energy efficiency of periodic hopping. These challenges are tackled with the reinforcement learning method Policy Improvement with Path Integrals (PI2) in a model-free approach to learn parameterized motor velocity trajectories as well as highlevel control parameters. The combination of simulation and hardware-based optimization allows to efficiently obtain optimal control policies in an up to 10-dimensional parameter space. The robotic leg learns to temporarily store energy in the elastic elements of the joints in order to improve the jump height and distance. In addition, we present a method to learn time-independent control policies and apply it to improve the energetic efficiency of periodic hopping

Efficient and Versatile Locomotion With Highly Compliant Legs

Drawing inspiration from nature, this paper introduces and compares two compliant robotic legs that are able to perform precise joint torque and position control, enable passive adaption to the environment, and allowfor the exploitation of natural dynamic motions.We report in detail on the design and control of both prototypes and elaborate specifically on the problem of precise foot placement during flight without the sacrifice of efficient energy storage during stance. This is achieved through an integrated design and control approach that incorporates series elastic actuation, series damping actuation, and active damping through torque control. The two legs are employed in efficient hopping/ running motions for which they achieve performance similar to humans or animals. This paper is concluded by a comparison of the various design choices with respect to performance and applicability, as well as an outlook on the usage of these legs in a fully actuated quadruped

High-performance series elastic actuation

textMobile legged robots have the potential to restructure many aspects of our lives in the near future. Whether for applications in household care, entertainment, or disaster response, these systems depend on high-performance actuators to improve their basic capabilities. The work presented here focuses on developing new high-performance actuators, specifically series elastic actuators, to address this need. We adopt a system-wide optimization approach, dealing with factors which influence performance at the levels of mechanical design, electrical system design, and control. Using this approach and based on a set of performance metrics, we produce an actuator, the UT-SEA, which achieves leading empirical results in terms of power-to-weight, force control, size, and system efficiency. We also develop general high-performance control techniques for both force- and position-controlled actuators, some of which were adopted for use on NASA-JSC's Valkyrie Humanoid robot and were used during DARPA's DRC Trials 2013 robotics competition.Electrical and Computer Engineerin

A Systematic Approach to the Design of Embodiment with Application to Bio-Inspired Compliant Legged Robots

Bio-inspired legged robots with compliant actuation can potentially achieve motion properties in real world scenarios which are superior to conventionally actuated robots. In this thesis, a methodology is presented to systematically design and tailor passive and active control elements for elastically actuated robots. It is based on a formal specification of requirements derived from the main design principles for embodied agents as proposed by Pfeifer et al. which are transfered to dynamic model based multi objective optimization problems. The proposed approach is demonstrated and applied for the design of a biomechanically inspired, musculoskeletal bipedal robot to achieve walking and human-like jogging

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