Knowing accurately the dynamic parameters of a manipulated object is required for common coordination strategies in physical human-robot interaction. Bias in object dynamics results in inaccurately calculated robot wrenches, which may disturb the human during interaction and bias the recognition of the human motion intention. This paper presents an identification strategy of object dynamics for physical human-robot interaction, which allows the tracking of desired human motion and inducing the motions necessary for parameter identification. The estimation of object dynamics is performed online and the estimator minimizes the least square error between the measured and estimated wrenches acting on the object. Identification-relevant motions are derived by analyzing the persistence of excitation condition, necessary for estimation convergence. Such motions are projected in the null space of the partial grasp matrix, relating the human and the robot redundant motion directions, to avoid disturbance of the human desired motion. The approach is evaluated in a physical human-robot object manipulation scenario.

Knowledge of the human grasp pose is crucial in common control schemes for human-robot object manipulation tasks. Biased estimates of the grasp pose cause undesired interaction wrenches on the human partner, which disturbs the interaction and the recognition of motion intention. A use of wearable motion sensors for tracking the human motion facilitates the grasp pose estimation without a global sensing system. This paper presents an approach for estimating an unknown grasp pose of the human using wearable motion sensors while minimizing undesired interaction wrenches applied to the human. A condition necessary for convergence of the estimator together with appropriate robot motion strategies are provided. Estimation of relative orientation and displacement is performed online and based on minimizing the error in the least-square sense. The estimation process does not rely on a global sensing system and it considers only the measurements of the velocity and acceleration of the cooperating partners in their respective local frames. The approach is experimentally evaluated in a physical human-robot interaction scenario.

This paper presents a method based on inverse kinematics with task specification for online human to humanoid motion imitation. We particularly focus on the problem of lifting and placing feet on the floor during the motion, allowing change of support during stepping or locomotion. The approach avoids the use of motion primitives that limit the robot motions to what had been learned. A direct transposition of movements is generated, allowing the robot to move freely in space as the human model does, at a velocity close to the reference one. The approach is validated on the humanoid robot NAO and shows very promising results for the use of online motion imitation.