Humanoid robots typically display locomotion patterns that include walking with flat foot-ground contact, and knees slightly bent. However, analysis of human gait indicate that several physiological mechanisms like stretched knees, heel-strike and toe push-off increase the step length and energetic efficiency of locomotion. This paper presents an implementation of two of those mechanisms, namely stretched knees and push-off, on a quasi-static whole-body balancing controller. The influence of such mechanisms on the kinematic capabilities of the DLR humanoid robot TORO is analyzed in different experiments, and their benefits are thoroughly discussed. As a result, the energetic savings of balancing with stretched knees are shown to be of reduced magnitude with respect to the overall power consumption of the robot, and the ability of TORO for negotiating stairs is greatly enhanced.

The paper addresses the static balancing and walking of elastically actuated legged robots. The control is realized by commanding the motor positions only and exploiting the bijective relation between motor and link positions at equilibrium under static external forces. The approach is formulated in a quite general framework first. The main implementation contribution is the definition of a body coordinate system and of an appropriate set of constraints, which leads to a fully determined system of equations. In addition to the desired COM and the vertical foot positions, which are defined by the walking task and the terrain, the imposed constraints are related to distances between individual legs. The controller is experimentally validated on a compliantly actuated quadruped.

Robotic teleoperation is a key technology for a wide variety of fields. Teleoperating a humanoid in particular is essential as it allows the user to act remotely on an interface designed especially for humans, e.g., in a space station, or operating tools and machinery in disaster scenarios. This paper presents a ‘task-relevant’ haptic interface for humanoid teleoperation, which bridges the gap between the task at hand and the balance of the robot. The operator is given command over the humanoid's hands and is informed through haptic cues about the impact of her/his potential actions on the robot’ stability. Moreover, a null-space autonomous controller acts in the operator's null-space to provide her/him with a wider workspace and help in the successful execution of the task. The architecture is designed to top an existing compliance controller for a torque-controlled humanoid robot. Experiments on the humanoid robot TORO are reported to demonstrate the feasibility and effectiveness of the approach.

This paper proposes an integration of multi-body and actuator control for multi-contact balancing for robots with highly elastic joints. Inspired by the structure preserving control concept for series-elastic fixed-base robots, the presented approach aims to minimize the control effort by keeping the system structure intact. Balancing on multiple contacts requires to solve the force distribution problem. In locomotion, contacts change quickly, requiring a swift redistribution of contact forces. This is a challenge for elastic robots as the actuator dynamics and limits prevent instantaneous changes of contact forces. The proposed dynamically consistent force distribution is implemented as a model predictive controller which resolves redundancy while complying with contact force and actuator constraints.

This paper presents a new method for planning and controlling dynamic multi-contact motions for humanoid robots. Our motion planner takes a sequence of multi-contact stances and generates closed-form reference trajectories for the robot center of mass (CoM) position, velocity, and acceleration, based on the concept of Divergent Component of Motion (DCM). The timing of the contact transitions and the end-effector trajectories are automatically computed such that the motion is feasible with respect to kinematic and dynamic constraints. We verify the constraints using a simplified model of the robot to achieve a very fast planner that finds a feasible solution within a few seconds. The reference trajectories serve as inputs to a passivity-based whole-body controller which includes a DCM controller for tracking the CoM trajectory. We demonstrate the robustness of our approach in simulation and experiments with the humanoid robot TORO.

Introducing humanoid robots in areas where space is limited, for example in search-and-rescue scenarios or industrial manufacturing, represents a huge challenge, especially when the environment is cluttered and unknown. The robot should be capable of utilizing multiple contact points distributed across the entire body and not just its feet and hands. Extra contacts on the whole body, for instance including the knees and elbows, enable the robot to increase its agility and robustness by enhancing the support polygon. This paper applies our passivity-based approach for hierarchical whole-body control including balancing to scenarios involving contacts distributed all over the body of the robot as required in confined spaces. The approach is experimentally validated on the torque-controlled humanoid robot TORO to demonstrate the general applicability of the presented framework.

Humanoid robots that need to traverse constrained and uncertain environments require a suitable combination of perception, planning and control. This paper presents an integrated pipeline that allows the robot to autonomously acquire visual information, define step locations, compute feasible multi-contact stances using hands and feet, and generate a motion plan to reach the desired goal even going through different contact states. The execution of the desired path is guaranteed through a passivity-based multi-contact controller. The approach is evaluated in simulations and experiments in different scenarios using the humanoid robot TORO.

This work presents a new approach to whole-body control for balancing and posture stabilization of humanoid robots utilizing an optimization of contact forces in combination with Model Predictive Control. To perform tasks that require multiple contacts, like manipulation or crawling, the controller allows the robot to use a subset of its end effectors to apply a desired wrench to the environment. The remaining end effectors are used for balancing while taking into account the wrenches originating from the manipulation. Due to the prediction the controller is able to react to changes in the control inputs in advance. This approach is evaluated in simulation with the humanoid robot TORO.

In this work we present a framework for kinesthetic teaching and iterative refinement of whole body motions. For detection of external forces we apply a momentum based disturbance observer known from manipulator control to the floating-base model of a humanoid robot. These external forces are used as a trigger for implementing a compliant behavior at the interaction point and are integrated into a predictive balancing algorithm. For representation of the motion data, a hidden Markov model is used, which allows for an iterative update of the discrete motion states as well as a smooth generation of continuous motion data. Finally, we present an application of these algorithms on the humanoid robot TORO.