Deriving Humanlike Arm Hand System Poses (original) (raw)
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Technical Report , Control Systems Lab, School of Mechanical Engineering, National Technical University of Athens, Greece., 2013
In this paper we provide directions, methods and metrics that can be used to synthesize a complete framework for mapping human to robot motion with Functional Anthropomorphism. Such a mapping can be used in order to perform skill transfer from humans to robot arm hand systems with arbitrary kinematics. The mapping schemes proposed, first guarantees the execution of specific functionalities by the robotic artifact in 3D space (task space functional constraints), and then optimizes anthropomorphism of robot motion. Human-likeness of robot motion is achieved through minimization of structural dissimilarity between human and robot arm hand system configurations. The proposed methodology is suitable for a wide range of applications ranging from learn by demonstration for autonomous grasp planning, to real time teleoperation and telemanipulation with robot arm hand systems in remote or dangerous environments.
Functional Anthropomorphism for Human to Robot Motion Mapping
IEEE International Symposium on Robot and Human Interactive Communication (RoMan), 2012
In this paper we propose a generic methodology for human to robot motion mapping for the case of a robotic arm hand system, allowing anthropomorphism. For doing so we discriminate between Functional Anthropomorphism and Perceptional Anthropomorphism, focusing on the first to achieve anthropomorphic solutions of the inverse kinematics for a redundant robot arm. Regarding hand motion mapping, a “wrist” (end-effector) offset to compensate for differences between human and robot hand dimensions is applied and the fingertips mapping methodology is used. Two different mapping scenarios are also examined: mapping for teleoperation and mapping for autonomous operation. The proposed methodology can be applied to a variety of human robot interaction applications, that require a special focus on anthropomorphism.
Human to humanoid motion conversion for dual-arm manipulation tasks
Robotica, 2018
SUMMARYA conversion process for the imitation of human dual-arm motion by a humanoid robot is presented. The conversion process consists of an imitation algorithm and an algorithm for generating human-like motion of the humanoid. The desired motions in Cartesian and joint spaces, obtained from the imitation algorithm, are used to generate the human-like motion of the humanoid. The proposed conversion process improves existing techniques and is developed with the aim to enable imitating of human motion with a humanoid robot, to perform a task with and/or without contact between hands and equipment. A comparative analysis shows that our algorithm, which takes into account the situation of marker frames and the position of joint frames, ensures more precise imitation than previously proposed methods. The results of our conversion algorithm are tested on the robot ROMEO through a complex “open/close drawer” task.
2003
Humanoid robotics is a new challenging field. To cooperate with human beings, humanoid robots not only have to feature human-like form and structure but, more importantly, they must possess human-like characteristics regarding motion, communication and intelligence. In this paper, we propose an algorithm for solving the inverse kinematics problem associated with the redundant robot arm of the humanoid robot ARMAR. The formulation of the problem is based on the decomposition of the workspace of the arm and on the analytical description of the redundancy of the arm. The solution obtained is characterized by its accuracy and low cost of computation. The algorithm is enhanced in order to generate human-like manipulation motions from object trajectories.
Autonomous motion planning of a hand-arm robotic system based on captured human-like hand postures
The paper deals with the problem of motion planning of anthropomorphic mechanical hands avoiding collisions and trying to mimic real human hand postures. The approach uses the concept of "principal motion directions" to reduce the dimension of the search space in order to obtain results with a compromise between motion optimality and planning complexity (time). Basically, the work includes the following phases: capturing the human hand workspace using a sensorized glove and mapping it to the mechanical hand workspace, reducing the space dimension by looking for the most relevant principal motion directions, and planning the hand movements using a probabilistic roadmap planner. The approach has been implemented for a four finger anthropomorphic mechanical hand (17 joints with 13 independent degrees of freedom) assembled on an industrial robot (6 independent degrees of freedom), and experimental examples are included to illustrate its validity.
Modeling human-likeness in approaching motions of dual-arm autonomous robots
2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), 2018
This paper addresses the problem of obtaining human-like motions with an anthropomorphic dual-arm torso assembled on a mobile platform. The focus is set on the coordinated movements of the robotic arms and the robot base while approaching a table to subsequently perform a bimanual manipulation task. For this, human movements are captured and mapped to the robot in order to compute the human dual-arm synergies. Since the demonstrated synergies change depending on the robot position, a recursive Cartesian-space discretization is presented based on these differences. Thereby, different movements of the arms are assigned to different regions of the Cartesian space. As an application example, a motion-planning algorithm exploiting this information is proposed and used.
Motion Planning by Demonstration With Human-Likeness Evaluation for Dual-Arm Robots
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017
The paper presents a planning procedure that allows an anthropomorphic dual-arm robotic system to perform a manipulation task in a natural human-like way by using demonstrated human movements. The key idea of the proposal is to convert the demonstrated trajectories into attractive potential fields defined over the configuration space and then use an RRT *-based planning algorithm that minimizes a path-cost function designed to bias the tree growth towards the human-demonstrated configurations. The paper presents a description of the proposed approach as well as results from a conceptual and a real application example, the latter using a real anthropomorphic dual-arm robotic system. A path-quality measure, based on first-order synergies (correlations between joint velocities) obtained from real human movements, is also proposed and used for evaluation and comparison purposes. The obtained results show that the paths obtained with the proposed procedure are more human-like.
Human-like Arm Motion Generation: A Review
Robotics, 2020
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 analysed 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 behavioural 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.
HumanLike Movement of an Anthropomorphic Robot: Problem Revisited
2011
Human-like movement is fundamental for natural human-robot interaction and collaboration. We have developed in a model for generating arm and hand movements an anthropomorphic robot. This model was inspired by the Posture-Based Motion-Planning Model of human reaching and grasping movements. In this paper we present some changes to the model we have proposed in [4] and test and compare different nonlinear constrained optimization techniques for solving the large-scale nonlinear constrained optimization problem that rises from the discretization of our time-continuous model. Furthermore, we test different time discretization steps.