Showing papers by "Thierry Fraichard published in 2017"

Human robot motion: A shared effort approach

Abstract: This paper is about Human Robot Motion (HRM), i.e. the study of how a robot should move among humans. This problem has often been solved by considering persons as moving obstacles, predicting their future trajectories and avoiding these trajectories. In contrast with such an approach, recent works have showed benefits of robots that can move and avoid collisions in a manner similar to persons, what we call human-like motion. One such benefit is that human-like motion was shown to reduce the planning effort for all persons in the environment, given that they tend to solve collision avoidance problems in similar ways. The effort required for avoiding a collision, however, is not shared equally between agents as it varies depending on factors such as visibility and crossing order. Thus, this work tackles HRM using the notion of motion effort and how it should be shared between the robot and the person in order to avoid collisions. To that end our approach learns a robot behavior using Reinforcement Learning that enables it to mutually solve the collision avoidance problem during our simulated trials.

Strong recursive feasibility in model predictive control of biped walking

Abstract: Realizing a stable walking motion requires satisfying a set of constraints. Model Predictive Control (MPC) is one of few suitable methods to handle such constraints. The capacity to satisfy constraints, which is usually called feasibility, is classically guaranteed recursively. In our applications, an important aspect is that the MPC scheme has to adapt continuously to the dynamic environment of the robot (e.g. collision avoidance, physical interaction). We aim therefore at guaranteeing recursive feasibility for all possible scenarios, which is called strong recursive feasibility. Recursive feasibility is classically obtained by introducing a terminal constraint at the end of the prediction horizon. Between two standard approaches for legged robot, in our applications we favor a capturable terminal constraint. When the robot is not really planning to stop and considers actually making a new step, recursive feasibility is not guaranteed anymore. We demonstrate numerically that recursive feasibility is actually guaranteed, even when a new step is added in the prediction horizon.