One of the most basic skills a robot should possess is predicting the effect of physical interactions with objects in the environment. This enables optimal action selection to reach a certain goal state. Traditionally, these dynamics are described by physics-based analytical models, which may however be very hard to find for complex problems. More recently, we have seen learning approaches that can predict the effect of more complex physical interactions directly from sensory input. However, it is an open question how far these models generalize beyond their training data. In this work, we analyse how analytical and learned models can be combined to leverage the best of both worlds. As physical interaction task, we use planar pushing, for which there exists a well-known analytical model and a large real-world dataset. We propose to use a neural network to convert the raw sensory data into a suitable representation that can be consumed by the analytical model and compare this approach to using neural networks for both, perception and prediction. Our results show that the combined method outperforms the purely learned version in terms of accuracy and generalization to push actions not seen during training. It also performs comparable to the analytical model applied on ground truth input values, despite using raw sensory data as input.
* Combining analytical models and learning The idea of using analytical models in combination with learning has also been explored in previous work. implemented a differentiable physics engine for rigid body dynamics in Theano and demonstrate how it can be used to train a neural network controller. In @cite_10 , the authors significantly improve Gaussian Process learning of inverse dynamics by using an analytical model of robot dynamics with fixed parameters as the mean function or as feature transform inside the covariance function of the GP's Kernel. Both works however do not cover visual perception. Most recently, used a graphics and physics engine to learn to extract object-based state representations in an unsupervised way: Given a sequence of images, a network learns to produce a state representation that is predicted forward in time using the physics engine. The graphics engine is used to render the predicted state and its output is compared to the next image as training signal. In contrast to the aforementioned work, we not only combine learning and analytical models, but also evaluate the advantages and limitations of this approach. @PARASPLIT
@cite_10
1998179438
In recent years, learning models from data has become an increasingly interesting tool for robotics, as it allows straightforward and accurate model approximation. However, in most robot learning approaches, the model is learned from scratch disregarding all prior knowledge about the system. For many complex robot systems, available prior knowledge from advanced physics-based modeling techniques can entail valuable information for model learning that may result in faster learning speed, higher accuracy and better generalization. In this paper, we investigate how parametric physical models (e.g., obtained from rigid body dynamics) can be used to improve the learning performance, and, especially, how semiparametric regression methods can be applied in this context. We present two possible semiparametric regression approaches, where the knowledge of the physical model can either become part of the mean function or of the kernel in a nonparametric Gaussian process regression. We compare the learning performance of these methods first on sampled data and, subsequently, apply the obtained inverse dynamics models in tracking control on a real Barrett WAM. The results show that the semiparametric models learned with rigid body dynamics as prior outperform the standard rigid body dynamics models on real data while generalizing better for unknown parts of the state space.