Gregory Dudek

farnoosh.faraji@mila.quebec

Github

Google Scholar

Steve Wen

Master's Research - McGill University

Principal supervisor :

Doina Precup

steve.wen@mila.quebec

Publications

Vision-Based Goal-Conditioned Policies for Underwater Navigation in the Presence of Obstacles.

Travis Manderson

Juan Camilo Gamboa Higuera

Stefan Wapnick

Jean-François Tremblay

Florian Shkurti

We present Nav2Goal, a data-efficient and end-to-end learning method for goal-conditioned visual navigation. Our technique is used to train … (see more)a navigation policy that enables a robot to navigate close to sparse geographic waypoints provided by a user without any prior map, all while avoiding obstacles and choosing paths that cover user-informed regions of interest. Our approach is based on recent advances in conditional imitation learning. General-purpose, safe and informative actions are demonstrated by a human expert. The learned policy is subsequently extended to be goal-conditioned by training with hindsight relabelling, guided by the robot's relative localization system, which requires no additional manual annotation. We deployed our method on an underwater vehicle in the open ocean to collect scientifically relevant data of coral reefs, which allowed our robot to operate safely and autonomously, even at very close proximity to the coral. Our field deployments have demonstrated over a kilometer of autonomous visual navigation, where the robot reaches on the order of 40 waypoints, while collecting scientifically relevant data. This is done while travelling within 0.5 m altitude from sensitive corals and exhibiting significant learned agility to overcome turbulent ocean conditions and to actively avoid collisions.

2020-07-11

Robotics: Science and Systems XVI (published)

Depth Prediction for Monocular Direct Visual Odometry

Ran Cheng

Christopher Agia

Depth prediction from monocular images with deep CNNs is a topic of increasing interest to the community. Advances have lead to models capab… (see more)le of predicting disparity maps with consistent scale, which are an acceptable prior for gradient-based direct methods. With this in consideration, we exploit depth prediction as a candidate prior for the coarse initialization, tracking, and marginalization steps of the direct visual odometry system, enabling the second-order optimizer to converge faster into a precise global minimum. In addition, the given depth prior supports large baseline stereo scenarios, maintaining robust pose estimations against challenging motion states such as in-place rotation. We further refine our pose estimation with semi-online loop closure. The experiments on KITTI demonstrate that our proposed method achieves state- of-the-art performance compared to both traditional direct visual odometry and learning-based counterparts.

2020-05-12

2020 17th Conference on Computer and Robot Vision (CRV) (published)

Navigation in the Service of Enhanced Pose Estimation

Travis Manderson

Ran Cheng

2020-01-22

Springer Proceedings in Advanced Robotics (published)

Seeing Through Your Skin: A Novel Visuo-Tactile Sensor for Robotic Manipulation

Francois Hogan

Sahand Rezaei-Shoshtari

M. Jenkin

Yashveer Girdhar

This work describes the development of the novel tactile sensor, named Semitransparent Tactile Sensor (STS), designed to enable reactive and… (see more) robust manipulation skills. The design, inspired from recent developments in optical tactile sensing technology, addresses a key missing features of these sensors: the ability to capture an “in the hand” perspective prior to and during the contact interaction. Whereas optical tactile sensors are typically opaque and obscure the view of the object at the critical moment prior to manipulator-object contact, we present a sensor that has the dual capabilities of acting as a tactile sensor and as a visual camera. This paper details the design and fabrication of the sensor, showcases its dual sensing capabilities, and introduces a simulated environment of the sensor within the PyBullet simulator.

2019-12-31

(published)

www.semanticscholar.org

Detecting GAN generated errors

Xiru Zhu

Fengdi Che

Tianzi Yang

Tzuyang Yu

Despite an impressive performance from the latest GAN for generating hyper-realistic images, GAN discriminators have difficulty evaluating t… (see more)he quality of an individual generated sample. This is because the task of evaluating the quality of a generated image differs from deciding if an image is real or fake. A generated image could be perfect except in a single area but still be detected as fake. Instead, we propose a novel approach for detecting where errors occur within a generated image. By collaging real images with generated images, we compute for each pixel, whether it belongs to the real distribution or generated distribution. Furthermore, we leverage attention to model long-range dependency; this allows detection of errors which are reasonable locally but not holistically. For evaluation, we show that our error detection can act as a quality metric for an individual image, unlike FID and IS. We leverage Improved Wasserstein, BigGAN, and StyleGAN to show a ranking based on our metric correlates impressively with FID scores. Our work opens the door for better understanding of GAN and the ability to select the best samples from a GAN model.

2019-12-01

ArXiv (preprint)

Learning Domain Randomization Distributions for Transfer of Locomotion Policies

Melissa Mozian

Juan Higuera

Domain randomization (DR) is a successful technique for learning robust policies for robot systems, when the dynamics of the target robot sy… (see more)stem are unknown. The success of policies trained with domain randomization however, is highly dependent on the correct selection of the randomization distribution. The majority of success stories typically use real world data in order to carefully select the DR distribution, or incorporate real world trajectories to better estimate appropriate randomization distributions. In this paper, we consider the problem of finding good domain randomization parameters for simulation, without prior access to data from the target system. We explore the use of gradient-based search methods to learn a domain randomization with the following properties: 1) The trained policy should be successful in environments sampled from the domain randomization distribution 2) The domain randomization distribution should be wide enough so that the experience similar to the target robot system is observed during training, while addressing the practicality of training finite capacity models. These two properties aim to ensure the trajectories encountered in the target system are close to those observed during training, as existing methods in machine learning are better suited for interpolation than extrapolation. We show how adapting the domain randomization distribution while training context-conditioned policies results in improvements on jump-start and asymptotic performance when transferring a learned policy to the target environment.

2019-06-01

ArXiv (preprint)

Semantic Mapping for View-Invariant Relocalization.

Jimmy Li

We propose a system for visual simultaneous localization and mapping (SLAM) that combines traditional local appearance-based features with s… (see more)emantically meaningful object landmarks to achieve both accurate local tracking and highly view-invariant object-driven relocalization. Our mapping process uses a sampling-based approach to efficiently infer the 3D pose of object landmarks from 2D bounding box object detections. These 3D landmarks then serve as a view-invariant representation which we leverage to achieve camera relocalization even when the viewing angle changes by more than 125 degrees. This level of view-invariance cannot be attained by local appearance-based features (e.g. SIFT) since the same set of surfaces are not even visible when the viewpoint changes significantly. Our experiments show that even when existing methods fail completely for viewpoint changes of more than 70 degrees, our method continues to achieve a relocalization rate of around 90%, with a mean rotational error of around 8 degrees.

2019-05-19

2019 International Conference on Robotics and Automation (ICRA) (published)

Planning in Dynamic Environments with Conditional Autoregressive Models

Johanna Hansen

Kyle Kastner

Aaron Courville

We demonstrate the use of conditional autoregressive generative models (van den Oord et al., 2016a) over a discrete latent space (van den Oo… (see more)rd et al., 2017b) for forward planning with MCTS. In order to test this method, we introduce a new environment featuring varying difficulty levels, along with moving goals and obstacles. The combination of high-quality frame generation and classical planning approaches nearly matches true environment performance for our task, demonstrating the usefulness of this method for model-based planning in dynamic environments.

2018-11-24

ArXiv (preprint)