This work presents a force controller for series elastic actuators that are used in gait robots such as exoskeletons, prostheses, and humanoid robots. Therefore, the controller needs to increase the bandwidth of the actuator, lower its apparent impedance for disturbance rejection or effortless interaction with a human user, and to stably interact with any (dynamic) environment. For gait, these environments are changing discontinuously, thus creating regular impacts. In this work, we propose the use of an inner loop PD controller to increase the bandwidth of the actuator, alongside an outer loop disturbance observer (DOB) to lower the apparent impedance of the actuator. To increase the controlled bandwidth of the actuator, we introduce a novel tuning method for the PD controller that allows for independent tuning of bandwidth and damping ratio of the controlled plant. The DOB, which is introduced to reject disturbances by lowering the apparent impedance, causes the apparent impedance to turn non-passive, resulting in potential contact and coupled instability of the actuator. To enable unconditionally stable interactions with any environment, we scale down the DOB contribution such that it lowers the apparent impedance while remaining passive. The proposed tuning method and DOB adaptation were evaluated on a test-setup by identifying the torque controller's transfer behavior and by identifying the apparent impedance of the actuator. The results of these tests showed that the proposed tuning method can separately tune bandwidth and damping ratio, while the DOB adaptation is able to trade-off the reduction of the apparent impedance with its passivity.

Background In the last two decades, lower-limb exoskeletons have been developed to assist human standing and locomotion. One of the ongoing challenges is the cooperation between the exoskeleton balance support and the wearer control. Here we present a cooperative ankle-exoskeleton control strategy to assist in balance recovery after unexpected disturbances during walking, which is inspired on human balance responses. Methods We evaluated the novel controller in ten able-bodied participants wearing the ankle modules of the Symbitron exoskeleton. During walking, participants received unexpected forward pushes with different timing and magnitude at the pelvis level, while being supported (Exo-Assistance) or not (Exo-NoAssistance) by the robotic assistance provided by the controller. The effectiveness of the assistive strategy was assessed in terms of (1) controller performance (Detection Delay, Joint Angles, and Exerted Ankle Torques), (2) analysis of effort (integral of normalized Muscle Activity after perturbation onset); and (3) Analysis of center of mass COM kinematics (relative maximum COM Motion, Recovery Time and Margin of Stability) and spatio-temporal parameters (Step Length and Swing Time). Results In general, the results show that when the controller was active, it was able to reduce participants’ effort while keeping similar ability to counteract and withstand the balance disturbances. Significant reductions were found for soleus and gastrocnemius medialis activity of the stance leg when comparing Exo-Assistance and Exo-NoAssistance walking conditions. Conclusions The proposed controller was able to cooperate with the able-bodied participants in counteracting perturbations, contributing to the state-of-the-art of bio-inspired cooperative ankle exoskeleton controllers for supporting dynamic balance. In the future, this control strategy may be used in exoskeletons to support and improve balance control in users with motor disabilities.

To enable the broad adoption of wearable robotic exoskeletons in medical and industrial settings, it is crucial they can adaptively support large repertoires of movements. We propose a new human-machine interface to simultaneously drive bilateral ankle exoskeletons during a range of "unseen" walking conditions and transitions that were not used for establishing the control interface. The proposed approach used person-specific neuromechanical models to estimate biological ankle joint torques in realtime from measured electromyograms (EMGS) and joint angles. We call this "neuromechanical model-based control" (NMBC). NMBC enabled six individuals to voluntarily control a bilateral ankle exoskeleton across six walking conditions, including all intermediate transitions, i.e., two walking speeds, each performed at three ground elevations. A single subject case-study was carried out on a dexterous locomotion tasks involving moonwalking. NMBC always enabled reducing biological ankle torques, as well as eight ankle muscle EMGs both within (22% torque;12% EMG) and between walking conditions (24% torque; 14% EMG) when compared to non-assisted conditions. Torque and EMG reductions in novel walking conditions indicated that the exoskeleton operated symbiotically, as an exomuscle controlled by the operator.s neuromuscular system. This opens new avenues for the systematic adoption of wearable robots as part of out-of-the-lab medical and occupational settings.

Individuals with neuromuscular injuries may fully benefit from wearable robots if a new class of wearable technologies is devised to assist complex movements seamlessly in everyday tasks. Among the most important tasks are locomotion activities. Current human-machine interfaces (HMI) are challenged in enabling assistance across wide ranges of locomoting tasks. Electromyography (EMG) and computational modelling can be used to establish an interface with the neuromuscular system. We propose an HMI based on EMGdriven musculoskeletal modelling that estimates biological joint torques in real-time and uses a percentage of these to dynamically control exoskeleton-generated torques in a taskindependent manner, i.e. no need to classify locomotion modes. Proof of principle results on one subject showed that this approach could reduce EMGs during exoskeleton-assisted even ground locomotion compared to transparent mode (i.e. zero impedance). Importantly, results showed that a substantial portion of the biological ankle joint torque needed to walk was transferred from the human to the exoskeleton. That is, while the total human-exoskeleton ankle joint was always similar between assisted and zero-impedance modes, the ratio between exoskeleton-generated and human-generated torque varied substantially, with human-generated torques being dynamically compensated by the exoskeleton during assisted mode. This is a first step towards natural, continuous assistance in a large variety of movements.

Mobility assistance robots can provide physical and balance support in order to prevent falls of elderly or patients. In this paper we propose a fall prevention approach for a mobility assistance robot equipped with a pair of actuated arms. The algorithm evaluates the user's Extrapolated Center of Mass (XCOM) and determines required supportive forces to be provided to the user in order to prevent user falls. We further present how the required forces are realized by the robot. Performance of the proposed approach is tested in experiments by a mobility assistance robot supporting subjects provoking falls in different directions.Milad Geravand and Wolfgang Rampeltshammer are with the Chair of

Robots are expected to possess similar capabilities that humans exhibit during close proximity dyadic interaction. Humans can easily adapt to each other in a multitude of scenarios, ensuring safe and natural interaction. Even though there have been attempts to mimic human motions for robot control, understanding the motion patterns emerging during dyadic interaction has been neglected. In this work, we analyze close-proximity human-human interaction and derive an ontology that describes a broad range of possible interaction scenarios by abstracting tasks and using insights from attention theory. This ontology enables us to group interaction behaviors into separate cases, each of which can be represented by a particular graph. Using imitation learning, we train unique interaction policies with recurrent neural networks for each case. The ontology offers a unified and generic approach to categorically analyze and learn close-proximity interaction behaviors that can both be utilized as base models for future studies and enhance natural human-robot collaboration. CCS Concepts: • Human-centered computing → Collaborative interaction; Interaction design theory, concepts and paradigms; • Computing methodologies → Robotic planning;

BackgroundIn the last two decades, lower-limb exoskeletons have been developed to assist human standing and locomotion. One of the ongoing challenges is the cooperation between the exoskeleton balance support and the wearer control. Here we present a cooperative ankle-exoskeleton control strategy to assist in balance recovery after unexpected disturbances during walking, which is inspired on human balance responses.MethodsWe evaluated the novel controller in ten able-bodied participants wearing the ankle modules of the Symbitron exoskeleton. During walking, participants received unexpected forward pushes with different timing and magnitude at the pelvis level, while being supported or not by the robotic assistance provided by the controller.ResultsThe results show that the controller was able to reduce participants’ effort while keeping similar ability to counteract and withstand the balance disturbances (average reduction of 10.09% in soleus activity, 5.20% in gastrocnemius medialis activity and 6.67% in gastrocnemius lateralis activity for the stance leg).ConclusionThe proposed controller was able to cooperate with the able-bodied participants in counteracting perturbations, contributing to the state-of-the-art of bio-inspired cooperative ankle exoskeleton controllers for supporting dynamic balance. In the future, this control strategy may be used in exoskeletons to support and improve balance control in users with motor disabilities.

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