Human to humanoid motion conversion for dual-arm manipulation tasks (original) (raw)
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In this paper, we present a system for the imitation of human motion on a humanoid robot, which is capable of incorporating both vision-based markerless and marker-based human motion capture techniques. Based on the so-called Master Motor Map, an interface for transferring motor knowledge between embodiments with different kinematics structure, the system is able to map human movement to a human-like movement on the humanoid while preserving the goal-directed characteristics of the movement. To attain an exact and goal-directed imitation of an observed movement, we introduce a reproduction module using non-linear optimization to maximize the similarity between the demonstrated human movement and the imitation by the robot. Experimental result using markerless and marker-based human motion capture data are given.
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Learning by imitation is becoming increasingly important for teaching humanoid robots new skills. The simplest form of imitation is behavior copying in which the robot is minimizing the difference between its perceived motion and that of the imitated agent. One problem that must be solved even in this simplest of all imitation tasks is calculating the learner's pose corresponding to the perceived pose of the agent it is imitating. This paper presents a general framework for solving this problem in closed form for the arms of a generalized humanoid robot of which most available humanoids are special cases. The paper also reports the evaluation of the proposed system for real and simulated robots.
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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.
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In this paper, we present a framework for perception, visualization, reproduction and recognition of human motion. On the perception side, various human motion capture systems exist, all of them having in common to calculate a sequence of configuration vectors for the human model in the core of the system. These human models may be 2D or 3D kinematic models, or on a lower level, 2D or 3D positions of markers. However, for appropriate visualization in terms of a 3D animation, and for reproduction on an actual robot, the acquired motion must be mapped to the target 3D kinematic model. On the understanding side, various action and activity recognition systems exist, which assume input of different kinds. However, given human motion capture data in terms of a high-dimensional 3D kinematic model, it is possible to transform the configurations into the appropriate representation which is specific to the recognition module. We will propose a complete architecture, allowing the replacement of any perception, visualization, reproduction module, or target platform. In the core of our architecture, we define a reference 3D kinematic model, which we intend to become a common standard in the robotics community, to allow sharing different software modules and having common benchmarks.
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Robots are rapidly becoming part of our lives, coexisting, interacting, and collaborating with humans in dynamic and unstructured environments. Mapping of human to robot motion has become increasingly important, as human demonstrations are employed in order to " teach " robots how to execute tasks both efficiently and anthropomorphically. Previous mapping approaches utilized complex analytical or numerical methods for the computation of the robot inverse kinematics (IK), without considering the humanlikeness of robot motion. The scope of this work is to synthesize humanlike robot trajectories for robot arm-hand systems with arbitrary kinematics, formulating a constrained optimization scheme with minimal design complexity and specifications (only the robot forward kinematics (FK) are used). In so doing, we capture the actual human arm-hand kinemat-ics, and we employ specific metrics of anthropomorphism, deriving humanlike poses and trajectories for various arm-hand systems (e.g., even for redundant or hyper-redundant robot arms and multifingered robot hands). The proposed mapping scheme exhibits the following characteristics: (1) it achieves an efficient execution of specific human-imposed goals in task-space, and (2) it optimizes anthropomorphism of robot poses, minimizing the structural dissimilarity/distance between the human and the robot arm-hand systems.
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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.
Human-like Arm Motion Generation: A Review
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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.