ERC Consolidator Grant · 2021
Model-based Reinforcement Learning for Versatile Robots in the Real World
REAL-RL proposes a path to autonomous robots that learn from experience. By learning to solve new and challenging tasks and exploiting their specific capabilities, they could become ubiquitous assistants to humans in an uncountable number of tasks. Current control strategies for robots are developed only for particular tasks and are not versatile. To ensure their functioning, it is necessary to have highly accurate physical models that precisely match all the essential aspects of the real world. REAL-RL follows a different path: a learning approach to robot control. The dominant direction in the field uses model-free reinforcement learning methods that need an incredible number of…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.