In future robotic applications in environments such as nursing houses, construction sites, private homes, etc, robots might need to take unpredicted physical actions according to the state of the users to overcome possible human errors. Referring to these actions as active physical humanrobot interactions (active pHRI), in this paper, the goal is to verify the possibility of identifying measurable interaction factors that could be used in future active pHRI controllers, by exploring and analyzing the state of the users during active pHRI. We hypothesize that active physical robot actions can cause measurable alterations in the physical and physiological data of the users, and that these measurements could be interpreted with users' personality and perceptions. We design an experiment where the participant uses the robot to play a visual puzzle game, during which, the robot takes unanticipated physical actions. We collect physiological and physical data, as well as outcomes of two state-of-the-art questionnaires on the perceptions of robots, CH-33 and Godspeed Series Questionnaires (GSQ), and a pre-experiment personality questionnaire, to relate the collected data with the users' perceptions and personality. The experiment outcomes show that we can extract a few factors related to personality, perception, physiological, and physical measurements. Even though we could not draw very clear correlations, these outcomes give fundamental insights for the design of novel pHRI experiments.

This paper deals with the problem of estimating the position of center of mass for a polyarticulated system (e.g. a humanoid robot or a human body), which makes contact with its environment. The only sensors providing measurements on this point are either interaction force sensors or kinematic reconstruction applied to a dynamic model of the system. We first study the observability of the center of mass position using these sensors and we show that the accuracy domain of each measurement can be easily described through a spectral analysis. We finally introduce an original approach based on the theory of complementary filtering to efficiently merge these input measurements and obtain an estimation of the center of mass position. This approach is extensively validated in simulation using a model of humanoid robot where (i) we confirm the spectral analysis of the signal errors and (ii) we show that the complementary filter offers a lower average reconstruction error than the classical Kalman filter. Some experimental applications of this filter on real signals are also presented.

A humanoid robot is underactuated and only relies on contacts with environment to move in the space. The ability to measure contact forces and torques enables then to predict the robot dynamics including balance. In classical cases, a humanoid robot is considered as a multi-body system with rigid limbs and joints and interactions with the environment are modeled as stiff contacts. Forces and torques at contacts are generally estimated with sensors which are expensive and sensitive to calibration errors. However, a robot is not perfectly rigid and contacts may have flexibilities. Therefore, external forces create geometric deformations of the body or its environment. These deformations may modify the robot dynamics and produce unwanted and unbalanced motions. Nonetheless, if we have a model of contact stiffness and are able to reconstruct reliably the geometric deformation, we can reconstruct forces and torques at contact. This study aims at estimating contact forces and torques and to observe the body kinematics of the robot with only an Inertial Measurements Unit (IMU). We show that we are able to reconstruct efficiently the position of the Center of Pressure (CoP) of the robot with only the IMU and proprioceptive data from the robot.

Abstract:The center of mass (CoM) is a key descriptor in the understanding and the analysis of bipedal locomotion. Some approaches are based on the premise that humans minimize the CoM vertical displacement. Other approaches express walking dynamics through the inverted pendulum model. Such approaches are contradictory in that they lead to two conflicting patterns to express the CoM motion: straight line segments for the first approaches and arcs of a circle for the second ones. In this paper, we show that CoM motion is a trade-off between both patterns. Specifically, CoM follows a "curtate cycloid", which is the curve described by a point rigidly attached to a wheel rolling on a flat surface. We demonstrate that all the three parameters defining a curtate cycloid only depend on the height of the subjects.