Deep learning has gone through massive growth in recent years. In many fields—computer vision, speech recognition, machine translation, game playing, and others—deep learning has brought unprecedented progress and become the method of choice. Will the same happen in robotics and automation? In a sense, it is already happening. Today, deep learning is often the most common keyword for work presented at major robotics conferences. At the same time, robots, as physical systems, pose unique challenges for deep learning in terms of sample efficiency and safety in real-world robot applications. With robots, data are abundant, but labels are sparse and expensive to acquire. Reinforcement learning in principle does not require data labeling but does require a significant number of iterations on real robots. Transferring the capabilities learned in simulation to real robots and collecting sufficient data for practical robot applications both present major challenges. Further, mistakes by robot learning systems are often much more costly than those by their counterparts in the virtual world. These mistakes may cause irreversible damage to robot hardware or, even worse, loss of human lives. Safety is thus paramount for robot learning systems.
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- Deep learning
- Machine learning
- Robot sensing systems
- Task analysis