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.
While GPs are the state of the art methods for robotic model learning, it is a well known fact that their computational complexity scales cubically with the number of training samples @math . This fact makes GPs unlikely to benefit from having big datasets that cover larger regions of the model's state space. A recent study @cite_2 shows that LSTM networks with a training time complexity of only @math , can outperform GPs for learning the inverse dynamics of a manipulator arm. The fact that LSTMs exploit the temporal correlations in the data makes this method suitable for learning dynamic models. Furthermore, their good computational efficiency and their scalability on big datasets is a major advantage for long-term learning settings.
@cite_2
2783813470
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.