Dynamic movement primitives prove to be a useful and effective way to represent a movement of a given agent. However, the original DMP formulation does not take the interaction among multiple agents into the consideration. Thus, many researchers focus on the development of a coupling term for the underlying dynamical system and its associated learning strategies. The result is highly dependent on the quality of the learning methods. In this paper, we present a new way to formulate and realize interactive movement primitive in a leader-follower configuration, where the relationship between the follower and the leader is explicitly represented via the new formulation. This new formulation does not only simplify the learning process, but it also meets the requirements of several applications. We separately tested our new formulation in the context of the handover task and the wiping task. The results prove the flexibility and simplicity of the new formulation.

Large-scale human motion databases are key for research questions ranging from human motion analysis and synthesis, biomechanics of human motion, data-driven learning of motion primitives, and rehabilitation robotics to the design of humanoid robots and wearable robots such as exoskeletons. In this paper we present a large-scale database of whole-body human motion with methods and tools, which allows a unifying representation of captured human motion, and efficient search in the database, as well as the transfer of subject-specific motions to robots with different embodiments. To this end, captured subject-specific motion is normalized regarding the subject's height and weight by using a reference kinematics and dynamics model of the human body, the master motor map (MMM). In contrast with previous approaches and human motion databases, the motion data in our database consider not only the motions of the human subject but the position and motion of objects with which the subject is interacting as well. In addition to the description of the MMM reference model, we present procedures and techniques for the systematic recording, labeling, and organization of human motion capture data, object motions as well as the subject–object relations. To allow efficient search for certain motion types in the database, motion recordings are manually annotated with motion description tags organized in a tree structure. We demonstrate the transfer of human motion to humanoid robots and provide several examples of motion analysis using the database.

In this paper, we address the question of generative knowledge construction from sensorimotor experience, which is acquired by exploration. We show how actions and their effects on objects, together with perceptual representations of the objects, are used to build generative models which then can be used in internal simulation to predict the outcome of actions. Specifically, the paper presents an experiential cycle for learning association between object properties (softness and height) and action parameters for the wiping task and building generative models from sensorimotor experience resulting from wiping experiments. Object and action are linked to the observed effect to generate training data for learning a non-parametric continuous model using Support Vector Regression. In subsequent iterations, this model is grounded and used to make predictions on the expected effects for novel objects which can be used to constrain the parameter exploration. The cycle and skills have been implemented on the humanoid platform ARMAR-IIIb. Experiments with set of wiping objects differing in softness and height demonstrate efficient learning and adaptation behavior of action of wiping.

In this paper, we present a grasp representation in task space exploiting position information of the fingertips. We propose a new way for grasp representation in the task space, which provides a suitable basis for grasp imitation learning. Inspired by neuroscientific findings, finger movement synergies in the task space together with fingertip positions are used to derive a parametric low-dimensional grasp representation. Taking into account correlating finger movements, we describe grasps using a system of virtual springs to connect the fingers, where different grasp types are defined by parameterizing the spring constants. Based on such continuous parameterization, all instantiation of grasp types and all hand preshapes during a grasping action (reach, preshape, enclose, open) can be represented. We present experimental results, in which the spring constants are merely estimated from fingertip motion tracking using a stereo camera setup of a humanoid robot. The results show that the generated grasps based on the proposed representation are similar to the observed grasps.

In this work, we present an integrated planner for collision-free single and dual arm grasping motions. The proposed Grasp-RRT planner combines the three main tasks needed for grasping an object: finding a feasible grasp, solving the inverse kinematics and searching a collision-free trajectory that brings the hand to the grasping pose. Therefore, RRT-based algorithms are used to build a tree of reachable and collision-free configurations. During RRT-generation, potential grasping positions are generated and approach movements toward them are computed. The quality of reachable grasping poses is scored with an online grasp quality measurement module which is based on the computation of applied forces in order to diminish the net torque.We also present an extension to a dual arm planner which generates bimanual grasps together with corresponding dual arm grasping motions. The algorithms are evaluated with different setups in simulation and on the humanoid robot ARMAR-III.