no code implementations • 8 Mar 2022 • Junchi Liang, Bowen Wen, Kostas Bekris, Abdeslam Boularias
This work aims to learn how to perform complex robot manipulation tasks that are composed of several, consecutively executed low-level sub-tasks, given as input a few visual demonstrations of the tasks performed by a person.
1 code implementation • 26 Jun 2021 • Andrew S. Morgan, Bowen Wen, Junchi Liang, Abdeslam Boularias, Aaron M. Dollar, Kostas Bekris
Highly constrained manipulation tasks continue to be challenging for autonomous robots as they require high levels of precision, typically less than 1mm, which is often incompatible with what can be achieved by traditional perception systems.
no code implementations • 4 Aug 2020 • Junchi Liang, Abdeslam Boularias
This paper introduces an algorithm for discovering implicit and delayed causal relations between events observed by a robot at arbitrary times, with the objective of improving data-efficiency and interpretability of model-based reinforcement learning (RL) techniques.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 17 Jun 2018 • Junchi Liang, Abdeslam Boularias
This paper presents a new and efficient method for learning such representations.