Human-like arm motion generation: a review

In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analyzed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioral and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations.FCT Project UID/MAT/00013/2013FCT–Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Nonlinear optimization for human-like synchronous movements of a dual arm-hand robotic system

In previous work we have presented a model capable of generating human-like movements for a dual arm-hand robot involved in human-robot cooperative tasks. However, the focus was on the generation of reach-to-grasp and reach-to-regrasp bimanual movements and no synchrony in timing was taken into account. In this paper we extend the previous model in order to accomplish bimanual manipulation tasks by synchronously moving both arms and hands of an anthropomorphic robotic system. Specifically, the new extended model has been designed for two different tasks with different degrees of difficulty. Numerical results were obtained by the implementation of the IPOPT solver embedded in our MATLAB simulator

Human-Like Arm Motion Generation: A Review

In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analyzed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioral and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations

From Human Motor Control to Human-like Motion in Robotics: planning, learning and controlling manipulation in Anthropomorphic Robotic Systems

Tese de Doutoramento em Engenharia Eletrónica e de ComputadoresComo os robôs estão começando a fazer parte do nosso dia a dia, eles devem ser capazes de cooperar de maneira natural e eficiente com os humanos para serem socialmente aceitos. A morfologia e os movimentos semelhantes aos humanos são frequentemente considerados recursos essenciais para interações intuitivas entre humanos e robôs, uma vez que permitem que colegas humanos prevejam facilmente os movimentos robóticos. Este projeto de doutorado diz respeito ao projeto e ao desenvolvimento de uma estrutura modular que pode transferir as características motoras humanas típicas de planejamento, aprendizagem e controle dos movimentos dos membros superiores para braços robóticos antropomórficos. A maior parte do trabalho apresentado diz respeito ao módulo de planejamento de movimento, que é capaz de gerar trajetórias livres de colisão com características cinemáticas humanas. Mais especificamente, o processo de planejamento leva em consideração uma hierarquia dependente da tarefa de restrições espaciais e posturais modeladas como funções de custo e, mais importante, aborda a prevenção de obstáculos de uma maneira naturalística. Além disso, a introdução de uma separação temporal de um movimento específico em fases fornece um certo grau de precisão humana, que pode ser necessária em tarefas de manipulação complexas. Para quantificar a semelhança humana, medidas de suavidade e regularidades de movimento cinemáticas, que são aplicadas em estudos de controle motor humano, são aqui adotadas para distinguir entre movimentos bem coordenados e prejudicados. Testes com dois dispositivos robóticos em diferentes tarefas pick-and-place mostraram a capacidade do planejador proposto de gerar trajetórias de membros superiores semelhantes às humanas com um custo computacional que é suficientemente pequeno para permitir interações fluentes entre humanos e robôs. Na presença de pequenas perturbações esperadas do ambiente durante a execução de tarefas semelhantes, os mecanismos de aprendizagem podem aumentar a familiaridade com sequências semelhantes de ações, melhorando o desempenho do planejamento geral. Com o objetivo de mimetizar o ciclo de aprendizagem experiencial humano, o que permite ganhar familiaridade por meio da experiência em campo, também é proposto um aprendiz de movimento incremental adaptativo. Após sessões suficientes de treinamento, testes com movimentos de alcance demonstraram que este módulo é capaz de acelerar o processo de planejamento e se adaptar a perturbações consistentes do cenário. A fim de lidar com perturbações inesperadas do espaço de trabalho, como a mudança repentina do alvo da mão, os sinais de feedback permitem uma redefinição da trajetória dos membros superiores para a realização bem-sucedida da tarefa dada. Por este motivo, o estudo de controladores de loop de feedback também faz parte deste projeto de doutorado para rastrear movimentos planejados de membros superiores semelhantes aos humanos e reagir prontamente a eventuais perturbações imprevistas do espaço de trabalho inicial. A aplicação desses controladores em diferentes tarefas manipulativas demonstrou a capacidade de manter as características cinemáticas humanas das trajetórias desejadas na ausência de perturbações significativas. Quando o espaço de trabalho é perturbado, um movimento controlado pode se adaptar on-line e concluir com sucesso a tarefa dada, mas o nível de semelhança humana alcançado dificilmente pode ser discutido devido à falta de métricas válidas de comparação. A separação e a interconexão dos módulos de planejamento, aprendizagem e controle compõem uma estrutura estruturada de geração de movimento semelhante ao humano para manipuladores antropomórficos. Esta divisão das diferentes funcionalidades sugere direções coordenadas de investigação para uma integração mais completa das habilidades motoras humanas em dispositivos robóticos centrados no homem.As robots are starting to become part of our daily lives, they must be able to cooperate in a natural and efficient manner with humans to be socially accepted. Human-like morphology and movements are often considered key features for intuitive human-robot interactions since they allow human peers to easily predict robotic movements. This PhD project concerns the design and the development of a modular framework that can transfer typical human motor characteristics of planning, learning and controlling upper-limb movements to anthropomorphic robotic arms. The majority of the presented work regards the motion planning module, which is capable of generating collision-free trajectories with human-like kinematic features. More specifically, the planning process takes into account a task-dependent hierarchy of spatial and postural constraints modelled as cost functions and, importantly, addresses obstacles avoidance in a naturalistic manner. Additionally, the introduction of a temporal separation of a particular movement in phases provides a certain degree of human-like accuracy, which may be required in complex manipulative tasks. To quantify human-likeness, smoothness measures and kinematic movement regularities, which are applied in human motor control studies, are here adopted to distinguish between well-coordinated and impaired movements. Tests with two robotic devices in different pick-and-place tasks showed the ability of the proposed planner to generate human-like upper-limb trajectories with a computational cost that is sufficiently small to allow fluent human-robot interactions. In presence of small expected perturbations of the environment during the execution of similar tasks, learning mechanisms can increase the familiarity with similar sequences of actions by improving the general planning performance. With the purpose of mimicking the human experiential learning cycle, which allows to gain familiarity through experience on the field, an adaptive incremental motion learner is also proposed. After sufficient sessions of training, tests with reaching movements demonstrated that this module is able to speed up the planning process and adapt to consistent perturbations of the scenario. In order to cope with unexpected disturbances of the workspace, such as the sudden change of the hand target, feed-back signals allows a redefinition of a upper-limb trajectory for the successful accomplishment of the given task. For this reason, the study of feed-back loop controllers is also part of this PhD project for tracking planned human-like upper-limb movements and promptly react to eventual unforeseen perturbations of the initial workspace. The application of these controllers on different manipulative tasks demonstrated the capability of maintaining the human-like kinematic characteristics of the desired trajectories in absence of significant perturbations. When the workspace is disturbed, a controlled motion can on-line adapt and successfully conclude the given task, but the level of achieved human-likeness can be hardly discussed due to the lack valid metrics of comparison. The separation and the interconnection of the planning, learning and controlling modules compose a structured framework of human-like motion generation for anthropomorphic manipulators. This division of the different functionalities suggests coordinated directions of investigation towards a more complete integration of human motor skills into human-centred robotic devices.This work was partly funded by the EU Project FP7 Marie Curie NETT-Neural Engineering and Transformative Technologies (ID 289146), the FCT PhD grant (ref. SFRH/BD/114923/2016) and the FCT Project UID/MAT/00013/2013

Continual learning of human-like arm postures

Inspired from established human motor control theories, our HUMP algorithm plans upper-limb collisions-free movements for anthropomorphic systems, which show kinematic human-like features [1]. Related cognitive issues can be further resolved when robots act as they are familiar with their workspace and can take initiative faster than in the early onsets of a task. Here, a continual learning technique is proposed to improve the performance of the HUMP under uncertainties of the items in a given scenario. Given the locality of the optimization-based HUMP algorithm, a meaningful initial guess, predicted from similar past motion experiences, can significantly reduce the computational cost and put the robot into action arguably faster than in the first attempts of planning with inexperienced initial guesses. This prediction is proposed to be incrementally refined by an optimal locally weighted regression method that operates on datasets of situational features that are regularly updated as new movements are planned by the robot in similar scenarios. The proposed cyclic experiential learner is tested on the selection of optimal human-like target postures in a reaching task with a large obstacle obstructing the straight-line path towards a given target. Results demonstrate the capability of extracting meaningful situational features in few sessions of online learning with a very limited size of the datasets. Comparisons with simple Euclidean locally weighted regression and random initializations showed the capability of planning target configurations of better quality with less computational cost. The proposed approach also exhibits to be robust against the interferences of new incoming samples depicting slightly changed situations of the same task.FP7 -Seventh Framework Programme(UIDB/00319/2020

Statistical analysis on the human-likeness of 3D reaching movements in humanoid robots

Human-like motion is often considered a key feature for intuitive human-robot interactions. In fact, this feature allows human peers to easily predict the robot's intention, which is perfectly aligned with the paradigm of collaborative industries, contributing to more human-centric and resilient industries. The one-sixth power law (1/6-PL) is well known in human motor control. In this work, the Human-like Upper-limb Motion Planner is used to generate three-dimensional (3D) movements of an anthropomorphic robotic arm. By applying direct kinematics, the position and orientation of the hand of the robot is determined. Subsequently, the respective curvature, torsion and velocity are computed. From a total of 600 movements, divided in six sessions, non-linear regression models are fitted and validated, in order to obtain the slope in the log-space of these movements. A statistical analysis of the parameters of the 1/6-PL is performed, and parametric and non-parametric tests are used to compare the results in each of the six sessions.Eliana Costa e Silva has been supported by national funds through FCT - Fundação para a Ciência e Tecnologia through project UIDB/04728/2020 and Gianpaolo Gulletta through the PhD grant (ref. SFRH/BD/114923/2016)

A Human-like Upper-limb Motion Planner: Generating naturalistic movements for humanoid robots

As robots are starting to become part of our daily lives, they must be able to cooperate in a natural and efficient manner with humans to be socially accepted. Human-like morphology and motion are often considered key features for intuitive human–robot interactions because they allow human peers to easily predict the final intention of a robotic movement. Here, we present a novel motion planning algorithm, the Human-like Upper-limb Motion Planner, for the upper limb of anthropomorphic robots, that generates collision-free trajectories with human-like characteristics. Mainly inspired from established theories of human motor control, the planning process takes into account a task-dependent hierarchy of spatial and postural constraints modelled as cost functions. For experimental validation, we generate arm-hand trajectories in a series of tasks including simple point-to-point reaching movements and sequential object-manipulation paradigms. Being a major contribution to the current literature, specific focus is on the kinematics of naturalistic arm movements during the avoidance of obstacles. To evaluate human-likeness, we observe kinematic regularities and adopt smoothness measures that are applied in human motor control studies to distinguish between well-coordinated and impaired movements. The results of this study show that the proposed algorithm is capable of planning arm-hand movements with human-like kinematic features at a computational cost that allows fluent and efficient human–robot interactions.info:eu-repo/semantics/publishedVersio