Quadrupedal Robots with Stiff and Compliant Actuation

In the broader context of quadrupedal locomotion, this overview article introduces and compares two platforms that are similar in structure, size, and morphology, yet differ greatly in their concept of actuation. The first, ALoF, is a classically stiff actuated robot that is controlled kinematically, while the second, StarlETH, uses a soft actuation scheme based on Changedhighly compliant series elastic actuators. We show how this conceptual difference influences design and control of the robots, compare the hardware of the two systems, and show exemplary their advantages in different application

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

Walking and Running with StarlETH

This paper presents the latest advances we made in static and dynamic locomotion with our compliant quadrupedal robot StarlETH. It summarizes the robot design and outlines the different underlying control principles used to achieve sophisticated locomotion performance. The focus of the paper is put on experimental findings which illustrate that the applied actuation and control principles are a valuable approach to bring our robotic devices a step closer to their natural counterparts

Kinematic Batch Calibration for Legged Robots

This paper introduces a novel batch optimization based calibration framework for legged robots. Given a nondegenerate calibration dataset and considering the stochastic models of the sensors, the task is formulated as a maximum likelihood problem. In order to facilitate the derivation of consistent measurement equations, the trajectory of the robot and other auxiliary variables are included into the optimization problem. This formulation can be transformed into a nonlinear least squares problem which can be readily solved. Applied to our legged robot StarlETH, the framework estimates kinematic parameters (segment lengths, body dimensions, angular offsets), accelerometer and gyroscope biases, as well as full inter-sensor calibrations. The generic structure easily allows the inclusion of additional sensor modalities. Based on datasets obtained on the real robot the consistency and performance of the presented approach are successfully evaluated

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