Policy transfer via modularity

Non-prehensile manipulation, such as pushing, is an important function for robots to move objects and is sometimes preferred as an alternative to grasping. However, due to unknown frictional forces, pushing has been proven a difficult task for robots. We explore the use of reinforcement learning to train a robot to robustly push an object. In order to deal with the sample complexity of training such a method, we train the pushing policy in simulation and then transfer this policy to the real world. In order to ease the transfer from simulation, we propose to use modularity to separate the learned policy from the raw inputs and outputs; rather than training ``end-to-end," we decompose our system into modules and train only a subset of these modules in simulation. We further demonstrate that we can incorporate prior knowledge about the task into the state space and the reward function to speed up convergence. Finally, we introduce "reward guiding" to modify the reward function and further reduce the training time. We demonstrate, in both simulation and real-world experiments, that such an approach can be used to reliably push an object from many initial positions and orientations.Outgoin

Towards SLAM with an events-based camera

Event-based cameras have an incredible potential in real-time and real-world robotics. They would enable more efficient algorithms in applications where high demanding requirements, such as rapid dynamic motion and high dynamic range, make standard cameras run into problems rapid dynamic motion and high dynamic range. While traditional cameras are based in the frame-base paradigm - a shutter captures a certain amount of pictures per second -, the bio-inspired event cameras have pixels that respond independently to the change of log-intensity generating asynchronous events. An special appeal for this type of cameras is their low band-width, since the stream of events contain all the information getting rid of the redundancy. This sensors that mimic some properties of the human retina has microseconds latency and 120 dB dynamic range (in contrast to the 60 dB of the standard cameras). However, the current impact of the event cameras has been tiny due to the necessity of completely new algorithm, there is no global measurement of the intensity which would allow the use of current methods. The fact that an event corresponds to an asynchronous local intensity difference turns out to be a challenging problem if one wants to recover the motion as well as the scene. This article tries to illustrate the several problems that are needed to face when dealing with this problem and some of the different approaches taken. First of all, we will explain the generative model of the event camera and the preliminaries, followed by the different approaches. Finally will the conclusions and a glossary of the code.Preprin