Neural Controller Synthesis for Signal Temporal Logic Specifications Using Encoder-Decoder Structured Networks
This addresses inefficiency in control synthesis for robotics or autonomous systems by enabling reusable controllers, though it is incremental as it builds on existing neural methods for STL.
The paper tackles the problem of needing to retrain neural network controllers for each new signal temporal logic specification by proposing an encoder-decoder structured network with attention, which encodes STL formulas into vectors and outputs control signals, achieving efficacy in a path planning experiment.
In this paper, we propose a control synthesis method for signal temporal logic (STL) specifications with neural networks (NNs). Most of the previous works consider training a controller for only a given STL specification. These approaches, however, require retraining the NN controller if a new specification arises and needs to be satisfied, which results in large consumption of memory and inefficient training. To tackle this problem, we propose to construct NN controllers by introducing encoder-decoder structured NNs with an attention mechanism. The encoder takes an STL formula as input and encodes it into an appropriate vector, and the decoder outputs control signals that will meet the given specification. As the encoder, we consider three NN structures: sequential, tree-structured, and graph-structured NNs. All the model parameters are trained in an end-to-end manner to maximize the expected robustness that is known to be a quantitative semantics of STL formulae. We compare the control performances attained by the above NN structures through a numerical experiment of the path planning problem, showing the efficacy of the proposed approach.