Talk Christian Wolf June 25th, 2:30 pm

[Jakob Verbeek]

Dear all, 

Christian Wolf will give a talk at INRIA Montbonnot at 2:30 pm on June 25. 

Speaker: Christian Wolf

INRIA-Chroma, INSA-Lyon, CITI, LIRIS http://liris.cnrs.fr/christian.wolf

Title: Learning and Robotics Abstract: In this talk we address perception and navigation problems in robotics settings, in particular mobile terrestrial robots and intelligent vehicles. We focus on learning representations, which are structured and allow to reason on a high level on the presents of objects and actors in a scene and to take planification and control decisions. Two different methods will be compared, which both structure their state as metric maps in a bird’s eye view, updated through affine transforms given ego-motion. The first method combines Bayesian filtering and Deep Learning to fuse LIDAR input and monocular RGB input, resulting in a semantic occupancy grid centered on a vehicle. A deep network is trained to segment RGB input and to fuse it with Bayesian occupancy grids. The second method automatically learning robot navigation in 3D environments from interactions and reward using Deep Reinforcement Learning. Similar to the first mode, it keeps a metric map of the environment in a bird’s eye view, which is dynamically updated. Different from the first method, the semantic meaning of the map’s content is not determined before hand or learned from supervision. Instead, projective geometry is used as an inductive bias in deep neural networks. The content of the metric map is learned from interactions and reward, allowing the agent to discover regularities and object affordances from the task itself. We also present a new benchmark and a suite of tasks requiring complex reasoning and exploration in continuous, partially observable 3D environments. The objective is to provide challenging scenarios and a robust baseline agent architecture that can be trained on mid-range consumer hardware in under 24h. Solving our tasks requires substantially more complex reasoning capabilities than standard benchmarks available for this kind of environments.

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Jakob Verbeek
+33.6.28.06.31.36