NIPS 2016
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Neurorobotics: A Chance for New Ideas, Algorithms and Approaches

Elmar Rueckert · Martin Riedmiller

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Modern robots are complex machines with many compliant actuators and various types of sensors including depth and vision cameras, tactile electrodes and dozens of proprioceptive sensors. The obvious challenges are to process these high dimensional input patterns, memorize low dimensional representations of them and to generate the desired motor commands to interact in dynamically changing environments. Similar challenges exist in brain machine interfaces (BMIs) where complex prostheses with perceptional feedback are controlled, or in motor neuroscience where in addition cognitive features need to be considered. Despite this broad research overlap the developments happened mainly in parallel and were not ported or exploited in the related domains. The main bottleneck for collaborative studies has been a lack of interaction between the core robotics, the machine learning and the neuroscience communities.

Why is it now just the right time for interactions?

- Latest developments based on deep neural networks have advanced the capabilities of robotic systems by learning control policies directly from the high dimensional sensor readings.
- Many variants of networks have been recently developed including the integration of feedback through recurrent connections, the projection to different feature spaces, may be trained at different time scales and can be modulated through additional inputs.
- These variants can be the basis for new models and concepts in motor neuroscience, where simple feed forward structures were not sufficiently powerful.
- Robotic applications demonstrated the feasibility of such networks for real time control of complex systems, which can be exploited in BMIs.
- Modern robots and new sensor technologies require models that can integrate a huge amount of inputs of different dimension, at different rates and with different noise levels. The neuroscience communities face such challenges and develop sophisticated models that can be evaluated in robotic applications used as benchmarks.
- New learning rules can be tested on real systems in challenging environments.


- Convolutional Networks and Real-time Robotic and Prosthetic applications
- Deep Learning for Robotics and Prosthetics
- End-to-End Robotics / Learning
- Feature Representations for Big Data
- Movement Representations, Movement Primitives and Muscle Synergies
- Neural Network Hardware Implementation, Neuromorphic Hardware
- Recurrent Networks and Reservoirs for Control of high dimensional systems
- Reinforcement Learning and Bayesian Optimization in Neural Networks from multiple reward sources
- Sampling Methods and Spiking Networks for Robotics
- Theoretical Learning Concepts, Synaptic Plasticity Rules for Neural Networks

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