Multi-context model learning is crucial for marine robotics where several factors can cause disturbances to the system’s dynamics. This work addresses the problem of identifying multiple contexts of an AUV model. We build a simulation model of the robot from experimental data, and use it to fill in the missing data and generate different model contexts. We implement an architecture based on long-short-term-memory (LSTM) networks to learn the different contexts directly from the data. We show that the LSTM network can achieve high classification accuracy compared to baseline methods, showing robustness against noise and scaling efficiently on large datasets.
Inverse dynamics model learning is crucial for modern robots where analytic models cannot capture the complex dynamics of compliant actuators, elasticities, mechanical inaccuracies, frictional effects or sensor noise. However, such models are highly nonlinear and millions of samples are needed to encode a large number of motor skills. Thus, current state of the art model learning approaches like Gaussian Processes which scale exponentially with the data cannot be applied. In this work, we developed an inverse dynamics model learning approach based on a long-short-term-memory (LSTM) network with a time complexity of O(n). We evaluated the approach on a KUKA robot arm that was used in object manipulation skills with various loads. In a comparison to Gaussian Processes we show that LSTM networks achieve better prediction performances and that they can be trained on large datasets with more than 100,000 samples in a few seconds. Moreover, due to the small training batch size of for example 128 samples, the network can be continuously improved in life-long learning scenarios.