Motor Control and Robot Learning
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (10 December 2021) | Viewed by 3150
Special Issue Editors
Interests: robot imitation learning; learning by demonstration; manipulation learning; robot compliance; adaptation of robot motion; human–robot cooperation; humanoid robotics; applicative industrial robotics
Interests: industrial robots; collaborative robots; control theory; wearable robotics; interaction control; human-robot collaboration; AI; ML
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
It is our pleasure to announce the opening of a new Special Issue of Applied Science.
The main topic of the special issue is the advancement of robot learning and motor control algorithms and their application.
Tasks that seem natural to humans are often tricky for robots or artificial agents, furthermore, often they are difficult to implement – to program. Hard-to-engineer robot tasks are being tackled by learning algorithms not only in academic and research environments – robot learning has progressed into all aspects of robot applications and use, be it in industry, rehabilitation, assistive robotics, or in entertainment applications. Robot learning has made giant strides towards making robots capable of performing complex tasks and acting independently in unstructured environments. From learning by demonstration, reinforcement learning, policy search, evolutionary algorithms and the recent success of deep reinforcement learning algorithms, the field is continually pushing the boundaries. That robots are breaking out of factories has become yesterday’s news. Thus, the progress of robot learning has drastically changed the landscape of the applicability of robots. Going hand-in-hand with machine learning and exploiting deep learning techniques, have made robot learning one of the best prospects for technology with the potential to radically change our future. But to change the future, the foundations need to be firm.
This Special Issue is set to present the newest findings, approaches and results in the field of robot learning and motor control, providing an overview of the possibilities of today and the prospect for the future.
Dr. Andrej Gams
Dr. Loris Roveda
Guest Editors
Manuscript Submission Information
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Keywords
- Robot learning
- Motor control
- Deep neural networks
- Robot skill learning
- Perception–action coupling
- Motion adaptation
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