About
7
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Introduction
Domingo Esteban previously worked at the Dynamic Legged System (DLS) lab at Istituto Italiano di Tecnologia (IIT). He conducted research in Reinforcement Learning and Optimal Control in Robotics during his time there. He now works at ANYbotics, where he continues to contribute his expertise to the field.
Education
November 2015 - July 2019
September 2012 - July 2014
April 2004 - April 2009
Publications
Publications (7)
This work is on vision-based planning strategies for legged robots that separate locomotion planning into foothold selection and pose adaptation. Current pose adaptation strategies optimize the robot's body pose relative to given footholds. If these footholds are not reached, the robot may end up in a state with no reachable safe footholds. Therefo...
This article focuses on vision-based planning strategies for legged robots that separate locomotion planning into foothold selection and pose adaptation. Current pose adaptation strategies optimize the robot's body pose relative to
given
footholds. If these footholds are not reached, the robot may end up in a state with no reachable safe foothold...
A common strategy to deal with the expensive reinforcement learning (RL) of complex tasks is to decompose them into a collection of subtasks that are usually simpler to learn as well as reusable for new problems. However, when a robot learns the policies for these subtasks, common approaches treat every policy learning process separately. Therefore...
A common strategy to deal with the expensive reinforcement learning (RL) of complex tasks is to decompose them into a collection of subtasks that are usually simpler to learn as well as reusable for new problems. However, when a robot learns the policies for these subtasks, common approaches treat every policy learning process separately. Therefore...
The prohibitively amount of data required when learning complex nonlinear policies, such as deep neural networks, has been significantly reduced with guided policy search (GPS) algorithms. However, while learning the control policy, the robot might fail and therefore generate unacceptable guiding samples. Failures may arise, for example, as a conse...
Learning complex manipulation tasks often requires to collect a large training dataset to obtain a model of a specific skill. This process may become laborious when dealing with high-DoF robots, and even more tiresome if the skill needs to be learned by multiple robots. In this paper, we investigate how this learning process can be accelerated by u...
Postural control of humanoid robots during locomotion tasks has been typically focused in keeping balance by means of classic feedback control systems. The study of human behaviour in postural control reveals the existence of an anticipative component for postural control introduced by means of a feedforward system. This anticipative subsystem is i...
