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Found 4 papers, 1 papers with code
Learning Sensorimotor Primitives of Sequential Manipulation Tasks from Visual Demonstrations
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.
Vision-driven Compliant Manipulation for Reliable, High-Precision Assembly Tasks
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.
Learning Transition Models with Time-delayed Causal Relations
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
Task-Relevant Object Discovery and Categorization for Playing First-person Shooter Games
This paper presents a new and efficient method for learning such representations.
