TY - CONF T1 - Approximate hybrid model predictive control for multi-contact push recovery in complex environments T2 - IEEE RAS International Conference on Humanoid Robots Y1 - 2018 A1 - T. Marcucci A1 - R. Deits A1 - M. Gabiccini A1 - A. Bicchi A1 - R. Tedrake JF - IEEE RAS International Conference on Humanoid Robots ER - TY - CONF T1 - Parametric Trajectory Libraries for Online Motion Planning with Application to Soft Robots T2 - 18th International Symposium on Robotics Research Y1 - 2017 A1 - T. Marcucci A1 - M. Garabini A1 - Gasparri, G. M. A1 - Alessio Artoni A1 - M Gabiccini A1 - A. Bicchi AB -

In this paper we propose a method for online motion planning of constrained nonlinear systems. The method consists of three steps: the offline generation of a library of parametric trajectories via direct trajectory optimization, the online search in the library for the best candidate solution to the optimal control problem we aim to solve, and the online refinement of this trajectory. The last phase of this process takes advantage of a sensitivity-like analysis and guarantees to comply with the first-order approximation of the constraints even in case of active set changes. Efficiency of the trajectory generation process is discussed and a valid strategy to minimize online computations is proposed; together with this, an effective procedure for searching the candidate trajectory is also presented. As a case study, we examine optimal control of a planar soft manipulator performing a pick-and-place task: through simulations and experiments, we show how crucial online computation times are to achieve considerable energy savings in the presence of variability of the task to perform.

JF - 18th International Symposium on Robotics Research ER - TY - Generic T1 - Towards Minimum-Information Adaptive Controllers for Robot Manipulators T2 - American Control Conference, AMC2017 Y1 - 2017 A1 - T. Marcucci A1 - C. Della Santina A1 - M Gabiccini A1 - A. Bicchi KW - Robotics AB -

The aim of this paper is to move a step in the direction of determining the minimum amount of information needed to control a robot manipulator within the framework of adaptive control. Recent innovations in the state of the art show how global asymptotic trajectory tracking can be achieved despite the presence of uncertainties in the kinematic and dynamic models of the robot. However, a clear distinction between
which parameters can be included among the uncertainties, and which parameters can not, has not been drawn yet. Since most of the adaptive control algorithms are built on linearly parameterized models, we propose to reformulate the problem as finding a procedure to determine whether and how a given dynamical system can be linearly parameterized with respect to a specific set of parameters.
Within this framework, we show how the trajectory tracking problem of a manipulator can be accomplished with the only knowledge of the number of joints of the manipulator. As an illustrative example, we present the end-effector trajectory tracking control of a robot initialized with the kinematic model of a different robot.

JF - American Control Conference, AMC2017 PB - IEEE CY - May 24–26 2017, Seattle USA UR - https://ieeexplore.ieee.org/document/7963602 ER -