Dyadic behavior in co-manipulation :from humans to robots

To both decrease the physical toll on a human worker, and increase a robot’s environment perception, a human-robot dyad may be used to co-manipulate a shared object. From the premise that humans are efficient working together, this work’s approach is to investigate human-human dyads co-manipulating an object. The co-manipulation is evaluated from motion capture data, surface electromyography (EMG) sensors, and custom contact sensors for qualitative performance analysis. A human-human dyadic co-manipulation experiment is designed in which every human is instructed to behave as a leader, as a follower or neither, acting as naturally as possible. The experiment data analysis revealed that humans modulate their arm mechanical impedance depending on their role during the co-manipulation. In order to emulate the human behavior during a co-manipulation task, an admittance controller with varying stiffness is presented. The desired stiffness is continuously varied based on a scalar and smooth function that assigns a degree of leadership to the robot. Furthermore, the controller is analyzed through simulations, its stability is analyzed by Lyapunov. The resulting object trajectories greatly resemble the patterns seen in the human-human dyad experiment.Para tanto diminuir o esforço físico de um humano, quanto aumentar a percepção de um ambiente por um robô, um díade humano-robô pode ser usado para co-manipulação de um objeto compartilhado. Partindo da premissa de que humanos são eficientes trabalhando juntos, a abordagem deste trabalho é a de investigar díades humano-humano co-manipulando um objeto compartilhado. A co-manipulação é avaliada a partir de dados de um sistema de captura de movimentos, sinais de eletromiografia (EMG), e de sensores de contato customizados para análise qualitativa de desempenho. Um experimento de co-manipulação com díades humano-humano foi projetado no qual cada humano é instruído a se comportar como um líder, um seguidor, ou simplesmente agir tão naturalmente quanto possível. A análise de dados do experimento revelou que os humanos modulam a rigidez mecânica do braço a depender de que tipo de comportamento eles foram designados antes da co-manipulação. Para emular o comportamento humano durante uma tarefa de co-manipulação, um controle por admitância com rigidez variável é apresentado neste trabalho. A rigidez desejada é continuamente variada com base em uma função escalar suave que define o grau de liderança do robô. Além disso, o controlador é analisado por meio de simulações, e sua estabilidade é analisada pela teoria de Lyapunov. As trajetórias resultantes do uso do controlador mostraram um padrão de comportamento muito parecido ao do experimento com díades humano-humano

An Integrated Decision Making Approach for Adaptive Shared Control of Mobility Assistance Robots

© 2016, Springer Science+Business Media Dordrecht. Mobility assistance robots provide support to elderly or patients during walking. The design of a safe and intuitive assistance behavior is one of the major challenges in this context. We present an integrated approach for the context-specific, on-line adaptation of the assistance level of a rollator-type mobility assistance robot by gain-scheduling of low-level robot control parameters. A human-inspired decision-making model, the drift-diffusion Model, is introduced as the key principle to gain-schedule parameters and with this to adapt the provided robot assistance in order to achieve a human-like assistive behavior. The mobility assistance robot is designed to provide (a) cognitive assistance to help the user following a desired path towards a predefined destination as well as (b) sensorial assistance to avoid collisions with obstacles while allowing for an intentional approach of them. Further, the robot observes the user long-term performance and fatigue to adapt the overall level of (c) physical assistance provided. For each type of assistance a decision-making problem is formulated that affects different low-level control parameters. The effectiveness of the proposed approach is demonstrated in technical validation experiments. Moreover, the proposed approach is evaluated in a user study with 35 elderly persons. Obtained results indicate that the proposed gain-scheduling technique incorporating ideas of human decision-making models shows a general high potential for the application in adaptive shared control of mobility assistance robots

Haptic negotiation and role exchange for collaboration in virtual environments

We investigate how collaborative guidance can be realized in multi-modal virtual environments for dynamic tasks involving motor control. Haptic guidance in our context can be defined as any form of force/tactile feedback that the computer generates to help a user execute a task in a faster, more accurate, and subjectively more pleasing fashion. In particular, we are interested in determining guidance mechanisms that best facilitate task performance and arouse a natural sense of collaboration. We suggest that a haptic guidance system can be further improved if it is supplemented with a role exchange mechanism, which allows the computer to adjust the forces it applies to the user in response to his/her actions. Recent work on collaboration and role exchange presented new perspectives on defining roles and interaction. However existing approaches mainly focus on relatively basic environments where the state of the system can be defined with a few parameters. We designed and implemented a complex and highly dynamic multimodal game for testing our interaction model. Since the state space of our application is complex, role exchange needs to be implemented carefully. We defined a novel negotiation process, which facilitates dynamic communication between the user and the computer, and realizes the exchange of roles using a three-state finite state machine. Our preliminary results indicate that even though the negotiation and role exchange mechanism we adopted does not improve performance in every evaluation criteria, it introduces a more personal and human-like interaction model

Continuous role adaptation for human-robot shared control

In this paper, we propose a role adaptation method for human-robot shared control. Game theory is employed for fundamental analysis of this two-agent system. An adaptation law is developed such that the robot is able to adjust its own role according to the human’s intention to lead or follow, which is inferred through the measured interaction force. In the absence of human interaction forces, the adaptive scheme allows the robot to take the lead and complete the task by itself. On the other hand, when the human persistently exerts strong forces that signal an unambiguous intent to lead, the robot yields and becomes the follower. Additionally, the full spectrum of mixed roles between these extreme scenarios is afforded by continuous online update of the control that is shared between both agents. Theoretical analysis shows that the resulting shared control is optimal with respect to a two-agent coordination game. Experimental results illustrate better overall performance, in terms of both error and effort, compared to fixed-role interactions

Flexible assimilation of human's target for versatile human-robot physical interaction

Recent studies on the physical interaction between humans have revealed their ability to read the partner's motion plan and use it to improve one's own control. Inspired by these results, we develop an intention assimilation controller (IAC) that enables a contact robot to estimate the human's virtual target from the interaction force, and combine it with its own target to plan motion. While the virtual target depends on the control gains assumed for the human, we show that this does not affect the stability of the human-robot system, and our novel scheme covers a continuum of interaction behaviours from assistance to competition. Simulations and experiments illustrate how the IAC can assist the human or compete with them to prevent collisions. We demonstrate the IAC's advantages over related methods, such as faster convergence to a target, guidance with less force, safer obstacle avoidance and a wider range of interaction behaviours

Model-based Control of Upper Extremity Human-Robot Rehabilitation Systems

Stroke rehabilitation technologies have focused on reducing treatment cost while improving effectiveness. Rehabilitation robots are generally developed for home and clinical usage to: 1) deliver repetitive and stimulating practice to post-stroke patients, 2) minimize therapist interventions, and 3) increase the number of patients per therapist, thereby decreasing the associated cost. The control of rehabilitation robots is often limited to black- or gray-box approaches; thus, safety issues regarding the human-robot interaction are not easily considered. Furthermore, despite numerous studies of control strategies for rehabilitation, there are very few rehabilitation robots in which the tasks are implemented using optimal control theory. Optimal controllers using physics-based models have the potential to overcome these issues. This thesis presents advanced impedance- and model-based controllers for an end-effector-based upper extremity stroke rehabilitation robot. The final goal is to implement a biomechanically-plausible real-time nonlinear model predictive control for the studied rehabilitation system. The real-time term indicates that the controller computations finish within the sampling frequency time. This control structure, along with advanced impedance-based controllers, can be applied to any human-environment interactions. This makes them promising tools for different types of assistive devices, exoskeletons, active prostheses and orthoses, and exercise equipment. In this thesis, a high-fidelity biomechatronic model of the human-robot interaction is developed. The rehabilitation robot is a 2 degree-of-freedom parallelogram linkage with joint friction and backlash, and nonlinear dynamics. The mechatronic model of the robot with relatively accurate identified dynamic parameters is used in the human-robot interaction plant. Different musculoskeletal upper extremity, biomechanic, models are used to model human body motions while interacting with the rehabilitation robot model. Human-robot interaction models are recruited for model-in-loop simulations, thereby tuning the developed controllers in a structured resolution. The interaction models are optimized for real-time simulations. Thus, they are also used within the model-based control structures to provide biofeedback during a rehabilitation therapy. In robotic rehabilitation, because of physical interaction of the patient with a mechanical device, safety is a fundamental element in the design of a controller. Thus, impedance-based assistance is commonly used for robotic rehabilitation. One of our objectives is to achieve a reliable and real-time implementable controller. In our definition, a reliable controller is capable of handling variable exercises and admittance interactions. The controller should reduce therapist intervention and improve the quality of the rehabilitation. Hence, we develop advanced impedance-based assistance controllers for the rehabilitation robot. Overall, two types of impedance-based (i.e., hybrid force-impedance and optimal impedance) controllers are developed and tuned using model-in-loop simulations. Their performances are assessed using simulations and/or experiments. Furthermore, their drawbacks are discussed and possible methods for their improvements are proposed. In contrast to black/gray-box controllers, a physics-based model can leverage the inherent dynamics of the system and facilitate implementation of special control techniques, which can optimize a specific performance criterion while meeting stringent system constraints. Thus, we present model-based controllers for the upper extremity rehabilitation robot using our developed musculoskeletal models. Two types of model-based controllers (i.e., nonlinear model predictive control using external 3-dimensional musculoskeletal model or internal 2-dimensional musculoskeletal model) are proposed. Their performances are evaluated in simulations and/or experiments. The biomechanically-plausible nonlinear model predictive control using internal 2-dimensional musculoskeletal model predicts muscular activities of the human subject and provides optimal assistance in real-time experiments, thereby conforming to our final goal for this project

NEPTUNE: Non-Entangling Planning for Multiple Tethered Unmanned Vehicles

Despite recent progress on trajectory planning of multiple robots and path planning of a single tethered robot, planning of multiple tethered robots to reach their individual targets without entanglements remains a challenging problem. In this paper, we present a complete approach to address this problem. Firstly, we propose a multi-robot tether-aware representation of homotopy, using which we can efficiently evaluate the feasibility and safety of a potential path in terms of (1) the cable length required to reach a target following the path, and (2) the risk of entanglements with the cables of other robots. Then, the proposed representation is applied in a decentralized and online planning framework that includes a graph-based kinodynamic trajectory finder and an optimization-based trajectory refinement, to generate entanglement-free, collision-free and dynamically feasible trajectories. The efficiency of the proposed homotopy representation is compared against existing single and multiple tethered robot planning approaches. Simulations with up to 8 UAVs show the effectiveness of the approach in entanglement prevention and its real-time capabilities. Flight experiments using 3 tethered UAVs verify the practicality of the presented approach.Comment: Accepted for publication in IEEE Transaction on Robotic

Differential game theory for versatile physical human-robot interaction

The last decades have seen a surge of robots working in contact with humans. However, until now these contact robots have made little use of the opportunities offered by physical interaction and lack a systematic methodology to produce versatile behaviours. Here, we develop an interactive robot controller able to understand the control strategy of the human user and react optimally to their movements. We demonstrate that combining an observer with a differential game theory controller can induce a stable interaction between the two partners, precisely identify each other’s control law, and allow them to successfully perform the task with minimum effort. Simulations and experiments with human subjects demonstrate these properties and illustrate how this controller can induce different representative interaction strategies