The task of robotic mobile manipulation poses several scientific challenges that need to be addressed to execute complex manipulation tasks in unstructured environments, in which collaboration with humans might be required. Therefore, we present ALMA, a motion planning and control framework for a torque-controlled quadrupedal robot equipped with a six degrees of freedom robotic arm capable of performing dynamic locomotion while executing manipulation tasks. The online motion planning framework, together with a whole-body controller based on a hierarchical optimization algorithm, enables the system to walk, trot and pace while executing operational space end-effector control, reactive human-robot collaboration and torso posture optimization to increase the arm's workspace. The torque control of the whole system enables the implementation of compliant behavior, allowing a user to safely interact with the robot. We verify our framework on the real robot by performing tasks such as opening a door and carrying a payload together with a human.

Robots working in natural, urban, and industrial settings need to be able to navigate challenging environments. In this paper, we present a motion planner for the perceptive rough-terrain locomotion with quadrupedal robots. The planner finds safe footholds along with collision-free swing-leg motions by leveraging an acquired terrain map. To this end, we present a novel pose optimization approach that enables the robot to climb over significant obstacles. We experimentally validate our approach with the quadrupedal robot ANYmal by autonomously traversing obstacles such steps, inclines, and stairs. The locomotion planner re-plans the motion at every step to cope with disturbances and dynamic environments. The robot has no prior knowledge of the scene, and all mapping, state estimation, control, and planning is performed in real-time onboard the robot.

The development of robots that can dance has received considerable attention. However, they are often either limited to a pre-defined set of movements and music or demonstrate little variance when reacting to external stimuli, such as microphone or camera input. In this paper, we contribute with a novel approach allowing a legged robot to listen to live music while dancing in synchronization with the music in a diverse fashion. This is achieved by extracting the beat from an onboard microphone in real-time, and subsequently creating a dance choreography by picking from a user-generated dance motion library at every new beat. Dance motions include various stepping and base motions. The process of picking from the library is defined by a probabilistic model, namely a Markov chain, that depends on the previously picked dance motion and the current music tempo. Finally, delays are determined online by time-shifting a measured signal and a reference signal, and minimizing the least squares error with the time-shift as parameter. Delays are then compensated for by using a combined feedforward and feedback delay controller which shifts the robot whole-body controller reference input in time. Results from experiments on a quadrupedal robot demonstrate the fast convergence and synchrony to the perceived music.

Traditional legged robots are capable of traversing challenging terrain, but lack of energy efficiency when compared to wheeled systems operating on flat environments. The combination of both locomotion domains overcomes the trade-off between mobility and efficiency. Therefore, this paper presents a novel motion planner and controller which together enable a legged robot equipped with skates to perform skating maneuvers. These are achieved by an appropriate combination of planned reaction forces and gliding motions. Our novel motion controller formulates a Virtual Model Controller and an optimal contact force distribution which takes into account the nonholonomic constraints introduced by the skates. This approach has been tested on the torque-controllable robot ANY mal equipped with passive wheels and ice skates as end-effectors. We conducted experiments on flat and inclined terrain, whereby we show that skating motions reduces the cost of transport by up to 80 % with respect to traditional walking gaits.

Quadrupedal locomotion can be described as a constrained optimization problem that is very hard to solve due to the high dimensional, nonlinear and non-smooth system dynamics. In this paper, we propose a formulation that can be solved within few seconds using sequential quadratic programming. This method considers only a simplified model that just sufficiently represents the system dynamics. The output is a very coarse plan, which can be accurately and robustly followed on a real system using hierarchical whole-body control combined with inverted pendulum-based reactive stepping. Using the fully torque controllable quadrupedal robot ANYmal, we present successful experiments for walking, trotting, and gait transitions even under substantial external disturbances.

This paper presents a framework which allows a quadrupedal robot to execute dynamic gaits including trot, pace and dynamic lateral walk, as well as a smooth transition between them. Our method relies on an online ZMP based motion planner which continuously updates the reference motion trajectory as a function of the contact schedule and the state of the robot. The planner is coupled with a hierarchical whole-body controller which optimizes the whole-body motion and contact forces by solving a cascade of prioritized tasks. We tested our framework on ANYmal, a fully torque controllable quadrupedal robot which is actuated by series-elastic actuators.

The present paper introduces a manipulator that is developed to combine safe and dynamic interaction tasks. The system is built from lightweight carbon fiber links and novel high-performance series elastic actuator units that provide dynamic movement capability, low-impedance joint torque control, and inherent interaction safety. This enabled the implementation of a model-based direct force control method purely based on joint torque regulation. Using unified force and motion control, the end-effector position can be accurately and dynamically tracked in task space while acting safely upon (unexpected) contacts with the environment. The force control component is implemented in a novel way that shows reduced forces in comparison to existing methods when navigating across a surface of unpredictable orientation and friction. ANYpulator is tested using a haptic feedback method that renders the system dynamics and contact forces back to the user.

This paper introduces ANYmal, a quadrupedal robot that features outstanding mobility and dynamic motion capability. Thanks to novel, compliant joint modules with integrated electronics, the 30 kg, 0.5 m tall robotic dog is torque controllable and very robust against impulsive loads during running or jumping. The presented machine was designed with a focus on outdoor suitability, simple maintenance, and user-friendly handling to enable future operation in real world scenarios. Performance tests with the joint actuators indicated a torque control bandwidth of more than 70 Hz, high disturbance rejection capability, as well as impact robustness when moving with maximal velocity. It is demonstrated in a series of experiments that ANYmal can execute walking gaits, dynamically trot at moderate speed, and is able to perform special maneuvers to stand up or crawl very steep stairs. Detailed measurements unveil that even full-speed running requires less than 280 W, resulting in an autonomy of more than 2 h.

Legged robots require a robust and fast responding feet contact detection strategy. Common force sensors are often too heavy and can be easily damaged during impacts with the terrain. Therefore, it is desirable to detect a contact without a force sensor. This paper introduces a probabilistic contact detection strategy which considers full dynamics and differential/forward kinematics to maximize the use of available information for contact estimation. This papers shows that such strategy is much more accurate than the state-of-the-art strategy that only take one measure into account, with a quadrupedal robot.

Quadrupedal locomotion on sloped terrains poses different challenges than walking in a mostly flat environment. The robot's configuration needs to be explicitly controlled in order to avoid slipping and kinematic limits. To this end, information about the terrain's inclination is required for carefully planning footholds, the pose of the main body, and modulation of the ground reaction forces. This is even more important for dynamic trotting, as only two support legs are available to compensate for gravity and drive a desired motion. We propose a reliable method for estimating the parameters of the terrain quadrupedal robots move on, in the face of limited perception capabilities and drifting robot pose estimates. By fusing inertial measurements, kinematic data from joint encoders and contact information from force sensors, the local inclination can be robustly estimated and used to optimize the contact forces to reduce slippage. The estimated terrain information, namely the pitch and roll angles of the ground plane, is exploited in an extended version of our previous model-based control approach. Our improved control framework enabled StarlETH, a medium-sized, fully autonomous, torque-controllable quadrupedal robot, to trot on slopes of up to 21°.

Methods of optimizing a single trajectory are mature enough for planning in many applications. Yet such optimization methods applied to high Degree-Of-Freedom robots either consume too much time to be real-time or approximate the dynamics such that they lack physical consistency. In this paper, we present a method of precomputing optimized trajectories and compressing the information to get a compact representation of the optimal policy function. By varying the initial configuration of a robot and optimizing multiple trajectories, the controller gains knowledge about the optimal policy function. Such computation can be performed on a powerful workstation or even supercomputers instead of an onboard computer of the robot. The precomputed optimal trajectories are stored in a Single-hidden Layer Feedforward neural Network (SLFN) using Optimally Pruned Extreme Learning Machine (OP-ELM). This ensures minimal representation of the model and fast evaluation of the SLFN. We first explain our method using a simple time-optimal control problem with an analytical solution. We then demonstrate how this method can work even for high dimensional state by optimizing a foothold strategy of a full quadruped robot in simulation.