Currently for balance recovery, humans outperform humanoid robots which use hand-designed controllers in terms of the diverse actions. This study aims to close this gap by finding core control principles that are shared across ankle, hip, toe and stepping strategies by formulating experiments to test human balance recoveries and define criteria to quantify the strategy in use. To reveal fundamental principles of balance strategies, our study shows that a minimum jerk controller can accurately replicate comparable human behaviour at the Centre of Mass level. Therefore, we formulate a general Model-Predictive Control (MPC) framework to produce recovery motions in any system, including legged machines, where the framework parameters are tuned for time-optimal performance in robotic systems.

This paper presents a control framework to teleoperate a quadruped robot's foot for operator-guided haptic exploration of the environment. Since one leg of a quadruped robot typically only has 3 actuated degrees of freedom (DoFs), the torso is employed to assist foot posture control via a hierarchical whole-body controller. The foot and torso postures are controlled by two analytical Cartesian impedance controllers cascaded by a null space projector. The contact forces acting on supporting feet are optimized by quadratic programming (QP). The foot's Cartesian impedance controller may also estimate contact forces from trajectory tracking errors, and relay the force-feedback to the operator. A 7D haptic joystick, Sigma.7, transmits motion commands to the quadruped robot ANYmal, and renders the force feedback. Furthermore, the joystick's motion is bounded by mapping the foot's feasible force polytope constrained by the friction cones and torque limits in order to prevent the operator from driving the robot to slipping or falling over. Experimental results demonstrate the efficiency of the proposed framework.

This paper focuses on robustness to disturbance forces and uncertain payloads. We present a novel formulation to optimize the robustness of dynamic trajectories. A straightforward transcription of this formulation into a nonlinear programming problem is not tractable for state-of-the-art solvers, but it is possible to overcome this complication by exploiting the structure induced by the kinematics of the robot. The non-trivial transcription proposed allows trajectory optimization frameworks to converge to highly robust dynamic solutions. We demonstrate the results of our approach using a quadruped robot equipped with a manipulator.

An important issue when synthesizing legged locomotion plans is the combinatorial complexity that arises from gait pattern selection. Though it can be defined manually, the gait pattern plays an important role in the feasibility and optimality of a motion with respect to a task. Replacing human intuition with an automatic and efficient approach for gait pattern selection would allow for more autonomous robots, responsive to task and environment changes. To this end, we propose the idea of building a map from task to gait pattern selection for given environment and performance objective. Indeed, we show that for a 2D half-cheetah model and a quadruped robot, a direct mapping between a given task and an optimal gait pattern can be established. We use supervised learning to capture the structure of this map in a form of gait regions. Furthermore, we propose to construct a warm-starting trajectory for each gait region. We empirically show that these warm-starting trajectories improve the convergence speed of our trajectory optimization problem up to 60 times when compared with random initial guesses. Finally, we conduct experimental trials on the ANYmal robot to validate our method.

Legged robots have great potential to perform complex loco-manipulation tasks, yet it is challenging to keep the robot balanced while it interacts with the environment. In this paper we investigated the use of additional contact points for maximising the robustness of loco-manipulation motions. Specifically, body-ground contact was studied for its ability to enhance robustness and manipulation capabilities of quadrupedal robots. We proposed equipping the robot with prongs: small legs rigidly attached to the body which create body-ground contact at controllable point-contacts. The effect of these prongs on robustness was quantified by computing the Smallest Unrejectable Force (SUF), a measure of robustness related to Feasible Wrench Polytopes. We applied the SUF to evaluate the robustness of the system, and proposed an effective approximation of the SUF that can be computed at near-real-time speed. We developed a hierarchical quadratic programming based whole-body controller that can control stable interaction when the prongs are in contact with the ground. This novel prong concept and complementary control framework were implemented on hardware to validate their effectiveness by showing increased robustness and newly enabled loco-manipulation tasks, such as obstacle clearance and manipulation of a large object.

The main task of robotic grippers, holding an object, does not require work theoretically. Yet grippers consume significant amounts of energy in practice. This paper presents an approach for designing an energy-saving drive for robotic grippers employing a Statically Balanced Force Amplifier (SBFA) and a Non-backdrivable mechanism (NBDM). A novel metric (Grip Performance Metric) to systematically evaluate drives regarding their energy consumption, is used in the design phase; afterwards, the realization and testing of a prototype (REED, Robotic Energy-Efficient Drive) are presented. Results show that the actuation force can be reduced by 92%, resulting in energy-savings of 86% for an example task. This shows the potential of drives based on SBFAs and NBDMs to achieve energy-neutral grippers.

Real world applications require robots to operate in unstructured environments. This kind of scenarios may lead to unexpected environmental contacts or undesired interactions, which may harm people or impair the robot. Adjusting the behavior of the system through impedance control techniques is an effective solution to these problems. However, selecting an adequate impedance is not a straightforward process. Normally, robot users manually tune the controller gains with trial and error methods. This approach is generally slow and requires practice. Moreover, complex tasks may require different impedance during different phases of the task. This paper introduces an optimization algorithm for online planning of the Cartesian robot impedance to adapt to changes in the task, robot configuration, expected disturbances, external environment and desired performance, without employing any direct force measurements. We provide an analytical solution leveraging the mass-spring-damper behavior that is conferred to the robot body by the Cartesian impedance controller. Stability during gains variation is also guaranteed. The effectiveness of the method is experimentally validated on the quadrupedal robot ANYmal. The variable impedance helps the robot to tackle challenging scenarios like walking on rough terrain and colliding with an obstacle.

From which states and with what controls can a biped avoid falling or reach a given target state? What is the most robust way to do these? So as to help with the design of walking robot controllers, and perhaps give insights into human walking, we address these questions using two simple 2-D models: the inverted pendulum (IP) and linear inverted pendulum (LIP). Each has one state variable at mid-stance, i.e., hip velocity, and two state-dependent controls at each step, i.e., push-off magnitude and step length (IP) and step time and length (LIP). Using practical targets and constraints, we compute all combinations of initial states and control actions for the next step, such that the robot can, with the best possible future controls, avoid falling for n steps or reach a target within n steps. All such combinations constitute regions in the combined space of states and controls. Farther from the boundaries of these regions, the robot tolerates larger errors and disturbances. Furthermore, for these models, and thus possibly real bipeds, usually if it is possible to avoid falling, it is possible to reach the target, and if it is possible to reach the target, it is possible to do so in two steps.

High feedback gains cannot be used on all robots due to sensor noise, time delays or interaction with humans. The problem with low feedback gain controlled robots is that the accuracy of the task execution is potentially low. In this paper we investigate if trajectory optimization of feedback-feedforward controlled robots improves their accuracy. For rest-to-rest motions, we find the optimal trajectory indirectly by numerically optimizing the corresponding feedforward controller for accuracy. A new performance measure called the Manipulation Sensitivity Norm (MSN) is introduced that determines the accuracy under most disturbances and modeling errors. We tested this method on a two DOF robotic arm in the horizontal plane. The results show that for all feedback gains we tested, the choice for the trajectory has a significant influence on the accuracy of the arm (viz. position errors being reduced from 2.5 cm to 0.3 cm). Moreover, to study which features of feedforward controllers cause high or low accuracy, four more feedforward controllers were tested. Results from those experiments indicate that a trajectory that is smooth or quickly approaches the goal position will be accurate.

Most conventional robotic arms depend on sensory feedback to perform their tasks. When feedback is inaccurate, slow or otherwise unreliable, robots should behave more like humans: rely on feedforward instead. This paper presents an approach to perform repetitive tasks with robotic arms, without the need for feedback (i.e. the control is open loop). The cyclic motions of the repetitive tasks are analyzed using an approach similar to limit cycle theory. We optimize open loop control signals that result in open loop stable motions. This approach to manipulator control was implemented on a two DOF arm in the horizontal plane with a spring on the first DOF, of which we show simulation and hardware results. The results show that both in simulation and in hardware experiments, it is possible to create open loop stable cycles. However, the two resulting cycles are different due to model inaccuracies. We also show simulation and hardware results for an inverted pendulum, of which we have a more accurate model. These results show stable cycles that are the same in simulation and hardware experiments.

The dynamic feasibility of solutions to motion planning problems using Rapidly Exploring Random Trees depends strongly on the choice of the distance metric used while planning. The ideal distance metric is the optimal cost of traversal between two states in the state space. However, it is computationally intensive to find the optimal cost while planning. We propose a novel approach to overcome this barrier by using a supervised learning algorithm that learns a nonlinear function which is an estimate of the optimal cost, via offline training. We use the Iterative Linear Quadratic Regulator approach for estimating an approximation to the optimal cost and learn this cost using Locally Weighted Projection Regression. We show that the learnt function approximates the original cost with a reasonable tolerance and more importantly, gives a tremendous speed up of a factor of 1000 over the actual computation time. We also use the learnt metric for solving the pendulum swing up planning problem and show that our metric performs better than the popularly used Linear Quadratic Regulator based metric.

The common view on feedforward control is that it needs an accurate model in order to accurately predict a future state of the system. However, in this paper we show that there are model inaccuracies that do not affect the final position of a motion, when using the right feedforward controller. Having an accurate final position is the main requirement in the task we consider: a pick-and-place task. We optimized the feedforward controllers such that the effect of model inaccuracies on the final position was minimized. The system we studied is a one DOF robotic arm in the horizontal plane, of which we show simulation and hardware results. The results show that the errors in the final position can be reduced to approximately zero for an inaccurate Coulomb, viscous or torque dependent friction. Furthermore, errors in the final position can be reduced, but not to zero, for an inaccurate inertia or motor constant. In conclusion, we show that for certain model inaccuracies, no feedback is required to eliminate the effect of an inaccurate model on the final position of a motion.