Learning Coupled Forward-Inverse Models with Combined Prediction Errors @ICRA-cg8kk

Uploaded May 2018 | Updated September 2026, 2 weeks ago

ICRA 2018 Spotlight Video
Interactive Session Tue PM Pod T.4
Authors: Koert, Dorothea; Maeda, Guilherme Jorge; Neumann, Gerhard; Peters, Jan
Title: Learning Coupled Forward-Inverse Models with Combined Prediction Errors

Abstract:
Challenging tasks in unstructured environments require robots to learn complex models. Given a large amount of information, learning multiple simple models can offer an efficient alternative to a monolithic complex network. Training multiple models---that is, learning their parameters and their responsibilities---has been shown to be prohibitively hard as optimization is prone to local minima. To efficiently learn multiple models for different contexts, we thus develop a new algorithm based on expectation maximization (EM). In contrast to comparable concepts, this algorithm trains multiple modules of paired forward-inverse models by using the prediction errors of both forward and inverse models simultaneously. In particular, we show that our method yields a substantial improvement over only considering the errors of the forward models on tasks where the inverse space contains multiple solutions.

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