- Deep learning (DL) is a new research track within the field of machine learning, building architectures consisting of multiple layers of representations in order to learn high level abstractions.
- Reinforcement learning (RL) is one of the most promising AI paradigms for the future development of autonomous robots. RL allows a robot to learn from trial-and-error interactions with its environment.
- DL can be used as a strong perception model of the environment.
- RL is used as the learning paradigm for controling robot's actions.
- The combination of DL and RL allows a learning agent to control a system based only on visual inputs, using a deep neural network to extract relevant features from the images.
Deep Learning is used as the perception model for:
- Robots vision, aural and natural language comprehension
to:
- provide representation of the environment,
- and producing control signals.
- End-to-end system.
- No need of physical modeling.
- Learning good representations.
- Human-level control through deep reinforcement learning, Volodymyr Mnih et al. Nature 518, 529–533.
- Deep Learning for Detecting Robotic Grasps, Ian Lenz, Honglak Lee, Ashutosh Saxena. International Journal of Robotics Research (IJRR), 2014.
- Playing Atari with Deep Reinforcement Learning, Volodymyr Mnih et al. CoRR Vol. abs/1312.5602.
- End-to-End Training of Deep Visuomotor Policies, Sergey Levine et al. CoRR Vol. abs/1504.00702.
- Peter Vrancx, Deep reinforcement learning