This paper presents a novel design of a compact and light-weight robotic hand for a social humanoid robot. The proposed system is able to perform common hand gestures and self-adaptable grasps by mixing under-actuated and self-adaptable hand kinematics in a unique design. The hand answers the need for precise finger postures and sensor-less force feedback during gestures and for finger adaptation and autonomous force distribution during grasps. These are provided by a dual actuation system embodied within the palm and the fingers. Coexistence is ensured by compliant transmissions based on elastomer bars rather than classical tension springs, thanks to their high elastic coefficient at reduced sizes and strains. The proposed solution significantly reduces the weight and the size of the hand by using a reduced number of small actuators for gesturing and a single motor for grasping. The hand prototype (ALPHA) is realized to confirm the design feasibility and functional capabilities. It is controlled to provide safe human-robot interaction and preserve mechanical integrity in order to be embodied on a humanoid robot.

This paper presents a numerical method to conceive and design the kinematic model of an anthropomorphic robotic hand used for gesturing and grasping. In literature, there are few numerical methods for the finger placement of human-inspired robotic hands. In particular, there are no numerical methods, for the thumb placement, that aim to improve the hand dexterity and grasping capabilities by keeping the hand design close to the human one. While existing models are usually the result of successive parameter adjustments, the proposed method determines the fingers placements by mean of empirical tests. Moreover, a surgery test and the workspace analysis of the whole hand are used to find the best thumb position and orientation according to the hand kinematics and structure. The result is validated through simulation where it is checked that the hand looks well balanced and that it meets our constraints and needs. The presented method provides a numerical tool which allows the easy computation of finger and thumb geometries and base placements for a human-like dexterous robotic hand.

Reading other's emotions is the key of a successful human-human communication. If we imagine using robots in human environment, the ability of robots to express emotions will help human communicate with the artificial agents. In studies about robots' expressing emotions, conversations, facial expression and simple gestures are mainly used. However, another way to express emotions is necessary to enable the expression in various scenes. This study focused on emotions conveyed by gait and examined the effectiveness of expression of emotions of a humanoid robot during gait. In the experiment, we generated 5 kinds of robot's emotional gait and showed them to the participants. Then participants forecast robot's emotion. This experiment was performed in Japan and in France, to investigate some cultural differences. As a result, this study shows that it is possible to convey emotions by gait for a humanoid robot and also that there is a cultural difference in forecasting emotions.

When the motion of the human body is studied by means of marker-based motion capture systems, one of the main source of error in this analysis is caused by the markers movements with respect to the underlying bones due to skin motion and deformation. In the kinematics estimation of the lower limbs this error is particularly notable for the rotations in the frontal and axial planes. Many algorithms exist trying to compensate this problem, most of them giving satisfactory results in the estimation of the knee flexion/extension, but correcting the effects of the skin deformation in other rotations is still an open issue. We have implemented a new algorithm to compensate this problem. This method was evaluated on a motion of the human lower limbs and compared with another existing global approach. The results of this experiment showed that the proposed algorithm gives much better results in the analysis of human motion, particularly for the internal/external rotation of the knee.

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.

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This paper deals with the generation of motion pattern for humanoid robots vertical jump. The study concentrates on the landing phase of the jump which is the most demanding for the system because of high discontinuity of acceleration at impact time. We first decompose the motion in two functions. The first one starts just before landing and consists in maximizing the value of the discontinuity by reducing the feet/ground velocity. We set the feet, COM and feet/COM velocities according to the supposed height of the jump. At impact time, the second function uses measured data of ground reaction force (GRF) from the ankle force sensors to quickly stop the system while keeping its vertical equilibrium. The influence of each function and their respective parameters is discussed and analyzed, and their validity is tested using the model of an existing humanoid platform

This paper proposes adapting human jumping dynamics to humanoid robotic structures. Data obtained from human jumping phases and decomposition together with ground reaction forces (GRF) are used as model references. Moreover, bodies inertial forces are used as task constraints while optimizing energy to determine the humanoid robot posture and improve its jumping performances.