Implementing dynamic locomotion behaviors on legged robots requires a high-quality state estimation module. Especially when the motion includes flight phases, state-of-the-art approaches fail to produce reliable estimation of the robot posture, in particular base height. In this paper, we propose a novel approach for combining visual-inertial odometry (VIO) with leg odometry in an extended Kalman filter (EKF) based state estimator. The VIO module uses a stereo camera and IMU to yield low-drift 3D position and yaw orientation and drift-free pitch and roll orientation of the robot base link in the inertial frame. However, these values have a considerable amount of latency due to image processing and optimization, while the rate of update is quite low which is not suitable for low-level control. To reduce the latency, we predict the VIO state estimate at the rate of the IMU measurements of the VIO sensor. The EKF module uses the base pose and linear velocity predicted by VIO, fuses them further with a second high-rate IMU and leg odometry measurements, and produces robot state estimates with a high frequency and small latency suitable for control. We integrate this lightweight estimation framework with a nonlinear model predictive controller and show successful implementation of a set of agile locomotion behaviors, including trotting and jumping at varying horizontal speeds, on a torque-controlled quadruped robot.

This paper aims at showing the dynamic performance and reliability of the low-cost, open-access quadruped robot Solo-12, which is developed within the framework of Open Dynamic Robot Initiative. It presents the implementation of a state-of-the-art control pipeline, close to the one that was previously implemented on Mini Cheetah, which implements a model predictive controller based on the centroidal dynamics to compute desired contact forces in order to track a reference velocity. Different contributions are proposed to speed up the computation process, notably at the level of the state estimation and the whole body controller. Experimental results demonstrate that the robot closely follow the reference velocity while being highly reactive and able to recover from perturbations.

In the context of model-based reinforcement learning and control, a large number of methods for learning system dynamics have been proposed in recent years. The purpose of these learned models is to synthesize new control policies. An important open question is how robust current dynamics-learning methods are to shifts in the data distribution due to changes in the control policy. We present a real-robot dataset which allows to systematically investigate this question. This dataset contains trajectories of a 3 degrees-of-freedom (DOF) robot being controlled by a diverse set of policies. For comparison, we also provide a simulated version of the dataset. Finally, we benchmark a few widely-used dynamics-learning methods using the proposed dataset. Our results show that the iid test error of a learned model is not necessarily a good indicator of its accuracy under control policies different from the one which generated the training data. This suggests that it may be important to evaluate dynamics-learning methods in terms of their transfer performance, rather than only their iid error.

Recently several hierarchical inverse dynamics controllers based on cascades of quadratic programs have been proposed for application on torque controlled robots. They have important theoretical benefits but have never been implemented on a torque controlled robot where model inaccuracies and real-time computation requirements can be problematic. In this contribution we present an experimental evaluation of these algorithms in the context of balance control for a humanoid robot. The presented experiments demonstrate the applicability of the approach under real robot conditions (i.e. model uncertainty, estimation errors, etc). We propose a simplification of the optimization problem that allows us to decrease computation time enough to implement it in a fast torque control loop. We implement a momentum-based balance controller which shows robust performance in face of unknown disturbances, even when the robot is standing on only one foot. In a second experiment, a tracking task is evaluated to demonstrate the performance of the controller with more complicated hierarchies. Our results show that hierarchical inverse dynamics controllers can be used for feedback control of humanoid robots and that momentum-based balance control can be efficiently implemented on a real robot.

In this paper we present a concept and an evaluation of an ape-like robot which is quite similar to its biological model. Aim of our project LittleApe is to build a small and extreme lightweight robot that is capable of walking on two and four legs as well as of changing from a four-legged posture to a two-legged posture, manipulating small objects, and which is also able to climb. LittleApe is modelled with attributes of a chimpanzee regarding limb proportions, spinal column, centre of mass, walking pattern, and range of motion. The concept of LittleApe is tested in simulation while building the real system. Two aspects were chosen to evaluate the concept described in detail within this paper. The first aspect comprises the use of an evolutionary method and the comparison of different morphologies. Based on the results from the first one, the second aspect deals with the manoeuvrability of the LittleApe robot.

Suspicion of water ice deposits in the lunar south-polar region have sparked new interest into the earth's smaller companion, and robotic crater sample return missions are being considered by a number of space agencies. The difficult terrain with an inclination of over 30°, eternal darkness and temperatures of less than -173°C make this a difficult task. In this paper we present a novel, bio-inspired light-weight system design, which demonstrates a possible approach for such a mission. The robot managed to come first in the Lunar Robotic Challenge (LRC), organised by the European Space Agency (ESA) in October 2008. Using a remote operated robot, we demonstrated to climb into and out of a lunar-like crater with inclination of more than 35° on loose substrate, and performed the collection and delivery of a 100 g soil sample without the aid of external illumination.

RHex is a hexapod with compliant legs and only six actuated degrees of freedom. Its ability to traverse highly fractured and unstable terrain, as well ascend and descend a particular flight of stairs has already been documented. In this paper, we describe an open loop controller that enables our small robot (length: 51 cm, width: 20 cm, height: 12.7 cm, leg length: 16 cm) to reliably climb a wide range of regular, full-size stairs with no operator input during stair climbing. Experimental data of energy efficiency in a form of specific resistance during stair climbing is given. The results presented in this paper are based on a new half circle leg design that implements a passive, effective leg length change.