We propose here a novel approach to solve robot planning problems based on spiking neural network models. The method is motivated by recent neuroscience findings on how rodents create mental plans for maze navigation and is grounded in the framework of planning as probabilistic inference. In this thesis, we demonstrate that the proposed spiking neural network is a suitable alternative to classical approaches and comes with interesting features.
Neural networks can be used in massive parallel computing, e.g., when implemented in neuromorphic hardware. These brain-like chips consist of thousands of memory and processing units operating in parallel. However, we are lacking suitable learning rules and algorithms. The developments in this thesis provide first testable algorithms for real-world robot planning applications.
Arbitrary complex functions can be learned such as dynamic or kinematic models. For that, a spike dependent version of contrastive divergence was derived to learn non-linear functions with kinesthetic teaching.
We show that these models can scale to a six-dimensional KUKA robot system, where in addition to an existing two-dimensional task space planning model two additional models were developed. One of these models can be queried in both directions, enabling that forward and inverse models can be learned at the same time.
Obstacles of arbitrary shape can be encoded in form of repelling forces through synaptic inhibition. Sampling of movement plans is done 4 − 60 times faster than real-time, which allows for foraging robot control, preparing multiple alternative solutions and deciding online which plan to execute. With the additionally implemented online rejection sampling, we could achieve target reaching errors of 4% in the modelled operational area. Furthermore, the generated movement trajectories did not require any post processing. Using bidirectional feedback between task and joint space during planning, smooth and goal-directed movements were computed at the same time.