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**1 - 6**of**6**### Fault detection and isolation from uninterpreted data in robotic sensorimotor cascades

- In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Saint Paul, MN

"... robotic sensorimotor cascades ..."

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### 1 Bootstrapping bilinear models of simple Vehicles

"... Abstract—Learning and adaptivity will play a large role in robotics in the future, as robots transition from unstructured to unstructured environments that cannot be fully predicted or understood by the designer. Two questions that are open are how much it is possible to learn, and how much we shoul ..."

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Abstract—Learning and adaptivity will play a large role in robotics in the future, as robots transition from unstructured to unstructured environments that cannot be fully predicted or understood by the designer. Two questions that are open are how much it is possible to learn, and how much we should learn. The goal of bootstrapping is creating agents that are able to learn everything from scratch, including a “torque to pixels” models for its robotic body. Systems with such capabilities will be advantaged in terms of being resilient to unforeseen changes and deviations from prior assumptions. This paper considers the bootstrapping problem for a subset of the set of all robots. The Vehicles, inspired by Braitenberg’s work, are idealization of mobile robots equipped with a set of “canonical ” exteroceptive sensors (camera; range-finder; field-sampler). Their sensel-level dynamics are derived and shown to be surprising close. We describe the first instance of a bootstrapping agent that can learn the dynamics of a relatively large universe of systems, and use the models to solve well-defined tasks, with no parameter tuning or hand-designed features. We define the class of BDS models, which assume an instantaneous bilinear dynamics between observations and commands, and derive streaming-based bilinear strategies for them. We show in what sense the BDS dynamics approximates the set of Vehicles to guarantee success in the task of generalized servoing: driving the observations to a given goal snapshot. Simulations and experiments substantiate the theoretical results. I.

### Accurate recursive learning of uncertain diffeomorphism dynamics

"... Abstract — Diffeomorphisms dynamical systems are dynamical systems for which the state is an image and each commands induce a diffeomorphism of the state. These systems can approximate the dynamics of robotic sensorimotor cascades well enough to be used for problems such as planning in observations ..."

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Abstract — Diffeomorphisms dynamical systems are dynamical systems for which the state is an image and each commands induce a diffeomorphism of the state. These systems can approximate the dynamics of robotic sensorimotor cascades well enough to be used for problems such as planning in observations space. Learning of an arbitrary diffeomorphism from pairs of images is a high dimensional learning problem. This paper describes two improvements to the methods presented in previous work. The previous method had required O(ρ 4) memory as a function of the desired resolution ρ, which, in practice, was the main limitation to the resolution of the diffeomorphisms that could be learned. This paper describes an algorithm based on recursive refinement that lowers the memory requirement to O(ρ 2). Another improvement regards the estimation the diffeomorphism uncertainty, which is used to represent the sensor’s limited field of view; the improved method obtains a more accurate estimation of the uncertainty by checking the consistency of a learned diffeomorphism and its independently learned inverse. The methods are tested on two robotic systems (a pan-tilt camera and a 5-DOF manipulator). I.