5 accepted papers
In previous work, model-based reinforcement learning was applied to a real-world labyrinth game to demonstrate sample-efficient learning using world models. In this paper, we further enhance sample efficiency and autonomy by introducing selective reconstruction: instead of reconstructing the full vi
Motivated by the challenge of achieving rapid learning in physical environments, this paper presents the development and training of a robotic system designed to navigate and solve a labyrinth game using model-based reinforcement learning techniques. The method involves extracting low-dimensional ob…
This letter aims to show that robots equipped with a vision-based tactile sensor can perform dynamic manipulation tasks without prior knowledge of all the physical attributes of the objects to be manipulated. For this purpose, a robotic system is presented that is able to swing up poles of different
Data-driven approaches to tactile sensing aim to overcome the complexity of accurately modeling contact with soft materials. However, their widespread adoption is impaired by concerns about data efficiency and the capability to generalize when applied to various tasks. This paper focuses on both the…
The development of robots that can dance has received considerable attention. However, they are often either limited to a pre-defined set of movements and music or demonstrate little variance when reacting to external stimuli, such as microphone or camera input. In this paper, we contribute with a n…