A nice analysis of the particularities of deep learning when applied to robotics, where the need to act is principal (unlike in other disciplines such as computer vision)

Niko Sünderhauf, Oliver Brock, Walter Scheirer, Raia Hadsell, Dieter Fox, Jürgen Leitner, Ben Upcroft, Pieter Abbeel, Wolfram Burgard, Michael Milford, and Peter Corke, The limits and potentials of deep learning for robotics, The International Journal of Robotics Research Vol 37, Issue 4-5, pp. 405 – 420, DOI: 10.1177/0278364918770733.

The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning. We explain the need for better evaluation metrics, highlight the importance and unique challenges for deep robotic learning in simulation, and explore the spectrum between purely data-driven and model-driven approaches. We hope this paper provides a motivating overview of important research directions to overcome the current limitations, and helps to fulfill the promising potentials of deep learning in robotics.

Some quotes beyond the abstract:

Deep learning systems, e.g. for classification or detection, typically return scores from their softmax layers that are proportional to the system’s confidence, but are not calibrated probabilities, and therefore not useable in a Bayesian sensor fusion framework

If, for example, an object detection system is fooled by data outside of its training data distribution (Goodfellow et al., 2014; Nguyen et al., 2015a), the consequences for a robot acting on false, but high-confidence detections can be catastrophic

As the robot moves in its environment, the camera will observe the scene from different viewpoints, which poses both challenges and opportunities to a robotic vision system […] One of the biggest advantages robotic vision can draw from its embodiment is the potential to control the camera, move it, and change its viewpoint to improve its perception or gather additional information about the scene. This is in stark contrast to most computer vision scenarios […] As an extension of active vision, a robotic system could purposefully manipulate the scene to aid its perception

In his influential 1867 book on physiological optics, Von Helmholtz (1867) formulated the idea that humans use unconscious reasoning, inference or conclusion, when processing visual information. Since then, psychologists have devised various experiments to investigate these unconscious mechanisms (Goldstein and Brockmole, 2016), modernized Helmholtz’s original ideas (Rock, 1983), and reformulated them in the framework of Bayesian inference (Kersten et al., 2004).

The properties of model-based and deep-learned approaches can be measured along multiple dimensions, including the kind of representations used for reasoning, how generally applicable their solutions are, how robust they are in real-world settings, how efficiently they make use of data, and how computationally efficient they are during operation. Model-based approaches often rely on explicit models of objects and their shape, surface, and mass properties, and use these to predict and control motion through time. In deep learning, models are typically implicitly encoded via networks and their parameters. As a consequence, model-based approaches have wide applicability, since the physics underlying them are universal. However, at the same time, the parameters of these models are difficult to estimate from perception, resulting in rather brittle performance operating only in local basins of convergence. Deep learning, on the other hand, enables highly robust performance when trained on sufficiently large data sets that are representative of the operating regime of the system. However, the implicit models learned by current deep learning techniques do not have the general applicability of physics-based reasoning. Model-based approaches are significantly more data efficient, related to their smaller number of parameters. The optimizations required for model-based approaches can be performed efficiently, but the basin of convergence can be rather small. In contrast, deep-learned solutions are often very fast and can have very large basins of convergence. However, they do not perform well if applied in a regime outside the training data

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