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Florian Golemo

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Publications

Active Domain Randomization
Bhairav Mehta
Manfred Diaz
Florian Golemo
Christopher Pal
Liam Paull
Domain randomization is a popular technique for improving domain transfer, often used in a zero-shot setting when the target domain is unkno… (see more)wn or cannot easily be used for training. In this work, we empirically examine the effects of domain randomization on agent generalization. Our experiments show that domain randomization may lead to suboptimal, high-variance policies, which we attribute to the uniform sampling of environment parameters. We propose Active Domain Randomization, a novel algorithm that learns a parameter sampling strategy. Our method looks for the most informative environment variations within the given randomization ranges by leveraging the discrepancies of policy rollouts in randomized and reference environment instances. We find that training more frequently on these instances leads to better overall agent generalization. In addition, when domain randomization and policy transfer fail, Active Domain Randomization offers more insight into the deficiencies of both the chosen parameter ranges and the learned policy, allowing for more focused debugging. Our experiments across various physics-based simulated and a real-robot task show that this enhancement leads to more robust, consistent policies.
2020-05-11
Proceedings of the Conference on Robot Learning (published)
proceedings.mlr.press
proceedings.mlr.press
Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments
Martin Weiss
Simon Chamorro
Roger Girgis
Margaux Luck
Samira E. Kahou
Joseph P. Cohen
Derek Nowrouzezahrai
Doina Precup
Florian Golemo
Chris Pal
Millions of blind and visually-impaired (BVI) people navigate urban environments every day, using smartphones for high-level path-planning a… (see more)nd white canes or guide dogs for local information. However, many BVI people still struggle to travel to new places. In our endeavor to create a navigation assistant for the BVI, we found that existing Reinforcement Learning (RL) environments were unsuitable for the task. This work introduces SEVN, a sidewalk simulation environment and a neural network-based approach to creating a navigation agent. SEVN contains panoramic images with labels for house numbers, doors, and street name signs, and formulations for several navigation tasks. We study the performance of an RL algorithm (PPO) in this setting. Our policy model fuses multi-modal observations in the form of variable resolution images, visible text, and simulated GPS data to navigate to a goal door. We hope that this dataset, simulator, and experimental results will provide a foundation for further research into the creation of agents that can assist members of the BVI community with outdoor navigation.
2020-05-11
Proceedings of the Conference on Robot Learning (published)
doi.org
proceedings.mlr.press
Pix2Shape – Towards Unsupervised Learning of 3D Scenes from Images using a View-based Representation
Sai Rajeswar
Fahim Mannan
Florian Golemo
Jérôme Parent-lévesque
David Vázquez
Derek Nowrouzezahrai
Aaron Courville
We infer and generate three-dimensional (3D) scene information from a single input image and without supervision. This problem is under-expl… (see more)ored, with most prior work relying on supervision from, e.g., 3D ground-truth, multiple images of a scene, image silhouettes or key-points. We propose Pix2Shape, an approach to solve this problem with four components: (i) an encoder that infers the latent 3D representation from an image, (ii) a decoder that generates an explicit 2.5D surfel-based reconstruction of a scene from the latent code (iii) a differentiable renderer that synthesizes a 2D image from the surfel representation, and (iv) a critic network trained to discriminate between images generated by the decoder-renderer and those from a training distribution. Pix2Shape can generate complex 3D scenes that scale with the view-dependent on-screen resolution, unlike representations that capture world-space resolution, i.e., voxels or meshes. We show that Pix2Shape learns a consistent scene representation in its encoded latent space and that the decoder can then be applied to this latent representation in order to synthesize the scene from a novel viewpoint. We evaluate Pix2Shape with experiments on the ShapeNet dataset as well as on a novel benchmark we developed, called 3D-IQTT, to evaluate models based on their ability to enable 3d spatial reasoning. Qualitative and quantitative evaluation demonstrate Pix2Shape's ability to solve scene reconstruction, generation, and understanding tasks.
2020-03-19
International Journal of Computer Vision (unknown)
doi.org
arxiv.org
Unsupervised Learning of Dense Visual Representations
Pedro O. Pinheiro
Amjad Almahairi
Ryan Y. Benmalek
Florian Golemo
Aaron Courville
Contrastive self-supervised learning has emerged as a promising approach to unsupervised visual representation learning. In general, these m… (see more)ethods learn global (image-level) representations that are invariant to different views (i.e., compositions of data augmentation) of the same image. However, many visual understanding tasks require dense (pixel-level) representations. In this paper, we propose View-Agnostic Dense Representation (VADeR) for unsupervised learning of dense representations. VADeR learns pixelwise representations by forcing local features to remain constant over different viewing conditions. Specifically, this is achieved through pixel-level contrastive learning: matching features (that is, features that describes the same location of the scene on different views) should be close in an embedding space, while non-matching features should be apart. VADeR provides a natural representation for dense prediction tasks and transfers well to downstream tasks. Our method outperforms ImageNet supervised pretraining (and strong unsupervised baselines) in multiple dense prediction tasks.
2019-12-31
Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (published)
doi.org
arxiv.org
Sim-to-Real Transfer with Neural-Augmented Robot Simulation
Florian Golemo
Adrien Ali Taiga
Aaron Courville
Pierre-Yves Oudeyer
Despite the recent successes of deep reinforcement learning, teaching complex motor skills to a physical robot remains a hard problem. While… (see more) learning directly on a real system is usually impractical, doing so in simulation has proven to be fast and safe. Nevertheless, because of the "reality gap," policies trained in simulation often perform poorly when deployed on a real system. In this work, we introduce a method for training a recurrent neural network on the differences between simulated and real robot trajectories and then using this model to augment the simulator. This Neural-Augmented Simulation (NAS) can be used to learn control policies that transfer significantly better to real environments than policies learned on existing simulators. We demonstrate the potential of our approach through a set of experiments on the Mujoco simulator with added backlash and the Poppy Ergo Jr robot. NAS allows us to learn policies that are competitive with ones that would have been learned directly on the real robot.
2018-10-22
Proceedings of The 2nd Conference on Robot Learning (published)
proceedings.mlr.press
proceedings.mlr.press
Pix2Scene: Learning Implicit 3D Representations from Images
Sai Rajeswar
Fahim Mannan
Florian Golemo
David Vázquez
Derek Nowrouzezahrai
Aaron Courville
2018-09-26
(published)
openreview.net
openreview.net
Home: A Household Multimodal Environment
Simon Brodeur
Ethan Perez
Ankesh Anand
Florian Golemo
Luca Celotti
Florian Strub
Jean Rouat
Hugo Larochelle
Aaron Courville
We introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction… (see more) with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learning, generalization, and transfer. HoME is an open-source, OpenAI Gym-compatible platform extensible to tasks in reinforcement learning, language grounding, sound-based navigation, robotics, multi-agent learning, and more. We hope HoME better enables artificial agents to learn as humans do: in an interactive, multimodal, and richly contextualized setting.
2017-12-31
ICLR (Workshop) (published)
doi.org
openreview.net