activity
20142023
most citedContinuous-Discrete Reinforcement Learning for Hybrid Control in Robotics

27 citations · 106 across the 13 of their papers we have counts for

collaborators
Showing 2017Show all

5 papers · 1 filter

cs.RO2017

Modeling and Control of Humanoid Robots in Dynamic Environments: iCub Balancing on a Seesaw

Gabriele Nava, Daniele Pucci, Nuno Guedelha +4

Forthcoming applications concerning humanoid robots may involve physical interaction between the robot and a dynamic environment. In such scenario, classical balancing and walking…

cs.RO2017

A Receding Horizon Push Recovery Strategy for Balancing the iCub Humanoid Robot

Stefano Dafarra, Francesco Romano, Francesco Nori

Balancing and reacting to strong and unexpected pushes is a critical requirement for humanoid robots. We recently designed a capture point based approach which interfaces with a mo…

cs.RO2017★ 24 cited

Torque-Controlled Stepping-Strategy Push Recovery: Design and Implementation on the iCub Humanoid Robot

Stefano Dafarra, Francesco Romano, Francesco Nori

One of the challenges for the robotics community is to deploy robots which can reliably operate in real world scenarios together with humans. A crucial requirement for legged robot…

cs.RO2017

A Predictive Momentum-Based Whole-Body Torque Controller: Theory and Simulations for the iCub Stepping

Stefano Dafarra, Francesco Romano, Gabriele Nava +1

When balancing, a humanoid robot can be easily subjected to unexpected disturbances like external pushes. In these circumstances, reactive movements as steps become a necessary req…

cs.RO2017

A Whole-Body Software Abstraction layer for Control Design of free-floating Mechanical Systems

Francesco Romano, Silvio Traversaro, Daniele Pucci +3

In this paper, we propose a software abstraction layer to simplify the design and synthesis of whole-body controllers without making any preliminary assumptions on the control law…