Transferring motion from a human demonstrator to a humanoid robot is an important step toward developing robots that are easily programmable and that can replicate or learn from observed human motion. The so called motion retargeting problem has been well studied and several off-line solutions exist based on optimization approaches that rely on pre-recorded human motion data collected from a marker-based motion capture system. From the perspective of human robot interaction, there is a growing interest in online motion transfer, particularly without using markers. Such requirements have placed stringent demands on retargeting algorithms and limited the potential use of off-line and pre-recorded methods. To address these limitations, we present an online task space control theoretic retargeting formulation to generate robot joint motions that adhere to the robot's joint limit constraints, joint velocity constraints and self-collision constraints. The inputs to the proposed method include low dimensional normalized human motion descriptors, detected and tracked using a vision based key-point detection and tracking algorithm. The proposed vision algorithm does not rely on markers placed on anatomical landmarks, nor does it require special instrumentation or calibration. The current implementation requires a depth image sequence, which is collected from a single time of flight imaging device. The feasibility of the proposed approach is shown by means of online experimental results on the Honda humanoid robot — ASIMO.

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This paper introduces a kinematically constrained closed loop inverse kinematics algorithm for motion control of robots or other articulated rigid body systems. The proposed strategy utilizes gradients of collision and joint limit potential functions to arrive at an appropriate weighting matrix to penalize and dampen motion approaching constraint surfaces. The method is particularly suitable for self collision avoidance of highly articulated systems which may have multiple collision points among several segment pairs. In that respect, the proposed method has a distinct advantage over existing gradient projection based methods which rely on numerically unstable null-space projections when there are multiple intermittent constraints. We also show how this approach can be augmented with a previously reported method based on redirection of constraints along virtual surface manifolds. The hybrid strategy is effective, robust, and does not require parameter tuning. The efficacy of the proposed algorithm is demonstrated for a self collision avoidance problem where the reference motion is obtained from human observations. We show simulation and experimental results on the humanoid robot ASIMO.

In this paper we present a human-friendly control framework and an associated system architecture for performing compliant trajectory tracking of multimodal human gesture information on a position controlled humanoid robot in realtime. The contribution of this paper includes a system architecture and control methodology that enables real-time compliant control of humanoid robots from demonstrated human motion and speech inputs. The human motion consists of the body and head pose. The human body motion, represented by a set of Cartesian space motion descriptors, is captured using a single depth camera marker-less vision processing module. The human head pose , represented by two degrees of freedom, is estimated and tracked using a single CCD camera. The architecture also enables fine motion control through human speech commands processed by a dedicated speech processing system. Motion description from the three input modes are synchronized and retargeted to the joint space coordinates of the humanoid robot in real-time. The retargeted motion adheres to the robot's kinematic constraints and represents the reference joint motion that is subsequently executed by a model based compliant control framework through a torque to position transformation system. The compliant and low gain tracking performed by this framework renders the system physically safe and therefore friendly to humans interacting with the robot. Experiments were performed on the Honda humanoid robot and the results are presented here.