Justin Carpentier

The Yoyo-Man

The Yoyo-Man project is a research action tending to explore the synergies of anthropomorphic locomotion. The seminal hypothesis is to consider the wheel as a plausible model of bipedal walking. In this paper we report on preliminary results developed along three perspectives combining biomechanics, neurophysiology and robotics. From a motion capture data basis of human walkers we first identify the center of mass (CoM) as a geometric center from which the motions of the feet are organized. Then we show how rimless wheels that model most passive walkers are better controlled when equipped with a stabilized mass on top of them. CoM and head play complementary roles that define what we call the Yoyo-Man.

Center-of-Mass Estimation for a Polyarticulated System in Contact—A Spectral Approach

This paper discusses the problem of estimating the position of the 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 simulations by using a model of a humanoid robot through which we confirm the spectral analysis of the signal errors and 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.

TALOS: A new humanoid research platform targeted for industrial applications

The upcoming generation of humanoid robots will have to be equipped with state-of-the-art technical features along with high industrial quality, but they should also offer the prospect of effective physical human interaction. In this paper we introduce a new humanoid robot capable of interacting with a human environment and targeting industrial applications. Limitations are outlined and used together with the feedback from the DARPA Robotics Challenge, and other teams leading the field in creating new humanoid robots. The resulting robot is able to handle weights of 6 kg with an out-stretched arm, and has powerful motors to carry out fast movements. Its kinematics have been specially designed for screwing and drilling motions. In order to make interaction with human operators possible, this robot is equipped with torque sensors to measure joint effort and high resolution encoders to measure both motor and joint positions. The humanoid robotics field has reached a stage where robustness and repeatability is the next watershed. We believe that this robot has the potential to become a powerful tool for the research community to successfully navigate this turning point, as the humanoid robot HRP-2 was in its own time.

Learning Feasibility Constraints for Multicontact Locomotion of Legged Robots

Relying on reduced models is nowadays a standard cunning to tackle the computational complexity of multi-contact locomotion. To be really effective, reduced models must respect some feasibility constraints in regards to the full model. However, such kind of constraints are either partially considered or just neglected inside the existing reduced problem formulation. This work presents a systematic approach to incorporate feasibility constraints inside trajectory optimization problems. In particular, we show how to learn the kinematic feasibility of the centre of mass to be achievable by the whole-body model. We validate the proposed method in the context of multi-contact locomotion: we perform two stairs climbing experiments on two humanoid robots, namely the HRP-2 robot and the new TALOS platform.