Ricerc@Sapienza

In this paper we propose an anytime planning/replanning algorithm aimed at generating motions allowing a humanoid to fulfill an assigned task that implicitly requires stepping. The algorithm interleaves planning and execution intervals: a previously planned whole-body motion is executed while simultaneously planning a new solution for the subsequent execution interval. At each planning interval, a specifically designed randomized local planner builds a tree in configuration-time space by concatenating successions of CoM movement primitives. Such a planner works in two stages.

This paper presents a Model Predictive Control (MPC) scheme capable of generating a 3D gait for a humanoid robot. The proposed method starts from an assigned sequence of footsteps and generates online the trajectory of both the Zero Moment Point and Center of Mass. Starting from the moment balance (neglecting rotations) we derive a model characterizing all 3D trajectories that satisfy a linear differential equation along all three axes.

The efficient control of our body and success- ful interaction with the environment are possible through the integration of multisensory information. Brain–computer interface (BCI) may allow people with sensorimotor dis- orders to actively interact in the world. In this study, visual information was paired with auditory feedback to improve the BCI control of a humanoid surrogate. Healthy and spinal cord injured (SCI) people were asked to embody a humanoid robot and complete a pick-and- place task by means of a visual evoked potentials BCI system.