How do we make concrete progress towards de-signing robots that can navigate ethically sensitive contexts? Almost two decades after the word ‘roboethics’ was coined, translating interdisciplinary roboethics discussions into techni-cal design still remains a daunting task. This paper describes our first attempt at addressing these challenges through a roboethics-themed design competition. The design competition setting allowed us to (a) formulate ethical considerations as an engineering design task that anyone with basic programming skills can tackle; and (b) develop a prototype evaluation scheme that incorporates diverse normative perspectives of multiple stakeholders. The initial implementation of the competition was held online at the RO-MAN 2021 conference. The competition task involved programming a simulated mobile robot (TIAGo) that delivers items for individuals in the home environment, where many of these tasks involve ethically sensitive con-texts (e.g., an underage family member asks for an alcoholic drink). This paper outlines our experiences implementing the competition and the lessons we learned. We highlight design competitions as a promising mechanism to enable a new wave of roboethics research equipped with technical design solutions.

This article presents passive impedance control of flexible joint robots (FJRs) via inner-loop torque control of elastic joints. However, according to our theoretical analysis, the torque control methods of series elastic actuators (SEAs) are often limited by the fact that the acceleration signals are amplified by the control gains. Since the acceleration signals are often affected by differentiation noise, the analysis may become invalid in practice. To alleviate this limitation, we propose the use of the so-called series viscoelastic actuator (SvEA), which significantly reduces the acceleration amplification. Consequently, in contrast to the SEA case, the theoretical analysis of an SvEA-based FJR is valid in real implementations. We would like to highlight the fact that the theoretical analysis (more specifically, passivity analysis) is performed for nonlinear robot dynamics without linearization. As a result, the passive impedance controller can be realized more robustly with enhanced inner-loop torque control.

Human-Robot Interaction (HRI) user studies are challenging to evaluate and compare due to a lack of standardization and the infrastructure required to implement each study. The lack of experimental infrastructure also makes it difficult to systematically evaluate the impact of individual components (e.g., the quality of perception software) on overall system performance. This work proposes a framework to ease the implementation and reproducibility of human-robot interaction user studies. The framework utilizes ROS middleware and is implemented with four modules: perception, decision, action, and metrics. The perception module aggregates sensor data to be used by the decision and action modules. The decision module is the task-level executive and can be designed by the HRI researcher for their specific task. The action module takes subtask requests from the decision module and breaks them down into motion primitives for execution on the robot. The metrics module tracks and generates quantitative metrics for the study. The framework is implemented with modular interfaces to allow for alternate implementations within each module and can be generalized for a variety of tasks and human/robot roles. The framework is illustrated through an example scenario involving a human and a Franka Emika Panda arm collaboratively assembling a toolbox together.

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.

This paper presents methods that facilitate the implementation of dynamic walking on torque-controlled robots in real world experiments. The work uses the Divergent Component of Motion (DCM) for walking trajectory generation and control. The DCM controller is embedded into a whole-body controller (WBC) that produces a full-body walking behavior. While in simulation the combination of DCM and WBC is sufficient for achieving sophisticated walking gaits, during our initial experiments several real-world issues, detailed in this paper, prevented the original control framework from functioning. This work presents the improvements to the original control framework that enabled a breakthrough on the way to achieving torque-based dynamic walking on a real robot.

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 that the joint torque control capability can be enhanced by adding physical damper to a series elastic actuator (SEA). Joint torque tracking of standard SEA has known limitations that the torque dynamics has an relative order of two, and, as a consequence, the torque controller often requires acceleration feedback when the desired torque is defined by a function of velocity (for example, compliance control). This limitation can be removed by introducing physical damping, reducing the relative degree of torque dynamics by one. Based on this observation, we design a robust controller using the disturbance observer technique. The resulting control law is given by a feed-forward term combined with PI control. The proposed controller is verified in simulation and experiment.

Series-elastic and viscoelastic robots can provide performance gains in applications with high dynamics. Harnessing these, requires an understanding of the dynamics of the system, which can be gained using optimization-based methods. The result are motions which make optimal use of the intrinsic behavior, possibly exceeding the performance of an equivalent rigid-body robot. We present a collocation framework which enables both automatic computation of contact-switching patterns and allows the full utilization of the dynamics of the compliant system. The formulation also addresses the problem of redundant torque generation in viscoelastic actuators. The effectiveness of this method was demonstrated in simulations as well as experiments with a compliant bipedal robot. The approach is capable of providing gait primitives, longer gait sequences containing multiple steps as well as generating extremely dynamic motions, e.g. somersaults.

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.

Control of robot locomotion profits from the use of pre-planned trajectories. This paper presents a way to generalize globally optimal and dynamically consistent trajectories for cyclic bipedal walking. A small task-space consisting of stride-length and step time is mapped to spline parameters which fully define the optimal joint space motion. The paper presents the impact of different machine learning algorithms for velocity and torque optimal trajectories with respect to optimality and feasibility. To demonstrate the usefulness of the trajectories, a control approach is presented that allows general walking including transitions between points in the task-space.

In this paper the generation of walking gaits for biped robots is addressed as a nonlinear optimization problem. The latter presents an efficient formulation, which only requires parameterizing the joint states and does not require to integrate the equations of motion. The results of the optimization are applied to a real robot, with the aid of a suitable stabilizing controller. The final gain in optimized cost is assessed, for the real system. The experimental results confirm the effectiveness of the method.