CORDIS Project
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This project aims to develop machine learning algorithms for robotics that require less data for effective learning. By integrating reinforcement learning with model-based approaches, it seeks to enhance robot exploration capabilities in real-world environments.
The robotics industry is in the process of greater adoption of machine learning.
Recent reinforcement learning (RL) and AI breakthroughs, such as AlphaGo, rely on collecting large amounts of data.
Such methods are unsuitable for real robots which often can only afford a few trials.
Moreover, some states are unsafe to explore, e.g. running over a cliff.
Conversely, works such as PILCO combine Bayesian models with model-based RL to improve data efficiency.
Those frameworks typically thrive in smal…
MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV
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