David Meger

Valliappan Chidambaram Adaikkappan

PhD - McGill University

Google Scholar

Wesley Chung

PhD - McGill University

Co-supervisor :

Doina Precup

Farnoosh Faraji

PhD - McGill University

Co-supervisor :

Master's Research - McGill University

Co-supervisor :

Hsiu-Chin Lin

Zina Kamel

Master's Research - McGill University

Co-supervisor :

Hsiu-Chin Lin

Sahand Rezaei-Shoshtari

PhD - McGill University

Principal supervisor :

PhD - McGill University

Junming(Clark) Shi

Master's Research - McGill University

Steven Wang

Master's Research - McGill University

Harley Wiltzer

PhD - McGill University

Co-supervisor :

Marc Gendron-Bellemare

PhD - McGill University

Publications

3D Shape Reconstruction from Vision and Touch

Edward J. Smith

Roberto Calandra

Adriana Romero

Georgia Gkioxari

Jitendra Malik

Michal Drozdzal

When a toddler is presented a new toy, their instinctual behaviour is to pick it up and inspect it with their hand and eyes in tandem, clear… (see more)ly searching over its surface to properly understand what they are playing with. Here, touch provides high fidelity localized information while vision provides complementary global context. However, in 3D shape reconstruction, the complementary fusion of visual and haptic modalities remains largely unexplored. In this paper, we study this problem and present an effective chart-based approach to fusing vision and touch, which leverages advances in graph convolutional networks. To do so, we introduce a dataset of simulated touch and vision signals from the interaction between a robotic hand and a large array of 3D objects. Our results show that (1) leveraging both vision and touch signals consistently improves single-modality baselines; (2) our approach outperforms alternative modality fusion methods and strongly benefits from the proposed chart-based structure; (3) the reconstruction quality increases with the number of grasps provided; and (4) the touch information not only enhances the reconstruction at the touch site but also extrapolates to its local neighborhood.

2019-12-31

Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (published)

View-Invariant Loop Closure with Oriented Semantic Landmarks

Jimmy Li

Karim Koreitem

Recent work on semantic simultaneous localization and mapping (SLAM) have shown the utility of natural objects as landmarks for improving lo… (see more)calization accuracy and robustness. In this paper we present a monocular semantic SLAM system that uses object identity and inter-object geometry for view-invariant loop detection and drift correction. Our system's ability to recognize an area of the scene even under large changes in viewing direction allows it to surpass the mapping accuracy of ORB-SLAM, which uses only local appearance-based features that are not robust to large viewpoint changes. Experiments on real indoor scenes show that our method achieves mean drift reduction of 70% when compared directly to ORB-SLAM. Additionally, we propose a method for object orientation estimation, where we leverage the tracked pose of a moving camera under the SLAM setting to overcome ambiguities caused by object symmetry. This allows our SLAM system to produce geometrically detailed semantic maps with object orientation, translation, and scale.

2019-12-31

IEEE International Conference on Robotics and Automation (published)

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)

Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning

Sahand Rezaei-Shoshtari

Inna Sharf

Motivated by the recursive Newton-Euler formulation, we propose a novel cascaded Gaussian process learning framework for the inverse dynamic… (see more)s of robot manipulators. This approach leads to a significant dimensionality reduction which in turn results in better learning and data efficiency. We explore two formulations for the cascading: the inward and outward, both along the manipulator chain topology. The learned modeling is tested in conjunction with the classical inverse dynamics model (semi-parametric) and on its own (non-parametric) in the context of feed-forward control of the arm. Experimental results are obtained with Jaco 2 six-DOF and SARCOS seven-DOF manipulators for randomly defined sinusoidal motions of the joints in order to evaluate the performance of cascading against the standard GP learning. In addition, experiments are conducted using Jaco 2 on a task emulating a pouring maneuver. Results indicate a consistent improvement in learning speed with the inward cascaded GP model and an overall improvement in data efficiency and generalization.

2019-11-02

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (published)

Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales

Sanjay Thakur

Herke van Hoof

Gunshi Gupta

Neural Network based controllers hold enormous potential to learn complex, high-dimensional functions. However, they are prone to overfittin… (see more)g and unwarranted extrapolations. PAC Bayes is a generalized framework which is more resistant to overfitting and that yields performance bounds that hold with arbitrarily high probability even on the unjustified extrapolations. However, optimizing to learn such a function and a bound is intractable for complex tasks. In this work, we propose a method to simultaneously learn such a function and estimate performance bounds that scale organically to high-dimensions, non-linear environments without making any explicit assumptions about the environment. We build our approach on a parallel that we draw between the formulations called ELBO and PAC Bayes when the risk metric is negative log likelihood. Through our experiments on multiple high dimensional MuJoCo locomotion tasks, we validate the correctness of our theory, show its ability to generalize better, and investigate the factors that are important for its learning. The code for all the experiments is available at this https URL.

2019-10-22

ArXiv (preprint)

Deep learning for Aerosol Forecasting

Caleb Hoyne

S. Karthik Mukkavilli

Reanalysis datasets combining numerical physics models and limited observations to generate a synthesised estimate of variables in an Earth … (see more)system, are prone to biases against ground truth. Biases identified with the NASA Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) aerosol optical depth (AOD) dataset, against the Aerosol Robotic Network (AERONET) ground measurements in previous studies, motivated the development of a deep learning based AOD prediction model globally. This study combines a convolutional neural network (CNN) with MERRA-2, tested against all AERONET sites. The new hybrid CNN-based model provides better estimates validated versus AERONET ground truth, than only using MERRA-2 reanalysis.

2019-10-13

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)

Human Motion Prediction Via Pattern Completion in Latent Representation Space

Yi Tian Xu

Yaqiao Li

Inspired by ideas in cognitive science, we propose a novel and general approach to solve human motion understanding via pattern completion o… (see more)n a learned latent representation space. Our model outperforms current state-of-the-art methods in human motion prediction across a number of tasks, with no customization. To construct a latent representation for time-series of various lengths, we propose a new and generic autoencoder based on sequence-to-sequence learning. While traditional inference strategies find a correlation between an input and an output, we use pattern completion, which views the input as a partial pattern and to predict the best corresponding complete pattern. Our results demonstrate that this approach has advantages when combined with our autoencoder in solving human motion prediction, motion generation and action classification.

2019-05-28

2019 16th Conference on Computer and Robot Vision (CRV) (published)

GeoMetrics: Exploiting Geometric Structure for Graph-Encoded Objects

Edward J. Smith

Scott Fujimoto

Adriana Romero

Mesh models are a promising approach for encoding the structure of 3D objects. Current mesh reconstruction systems predict uniformly distrib… (see more)uted vertex locations of a predetermined graph through a series of graph convolutions, leading to compromises with respect to performance or resolution. In this paper, we argue that the graph representation of geometric objects allows for additional structure, which should be leveraged for enhanced reconstruction. Thus, we propose a system which properly benefits from the advantages of the geometric structure of graph encoded objects by introducing (1) a graph convolutional update preserving vertex information; (2) an adaptive splitting heuristic allowing detail to emerge; and (3) a training objective operating both on the local surfaces defined by vertices as well as the global structure defined by the mesh. Our proposed method is evaluated on the task of 3D object reconstruction from images with the ShapeNet dataset, where we demonstrate state of the art performance, both visually and numerically, while having far smaller space requirements by generating adaptive meshes

2019-05-23

Proceedings of the 36th International Conference on Machine Learning (published)

proceedings.mlr.press

Off-Policy Deep Reinforcement Learning without Exploration

Scott Fujimoto

Doina Precup

Many practical applications of reinforcement learning constrain agents to learn from a fixed batch of data which has already been gathered, … (see more)without offering further possibility for data collection. In this paper, we demonstrate that due to errors introduced by extrapolation, standard off-policy deep reinforcement learning algorithms, such as DQN and DDPG, are incapable of learning with data uncorrelated to the distribution under the current policy, making them ineffective for this fixed batch setting. We introduce a novel class of off-policy algorithms, batch-constrained reinforcement learning, which restricts the action space in order to force the agent towards behaving close to on-policy with respect to a subset of the given data. We present the first continuous control deep reinforcement learning algorithm which can learn effectively from arbitrary, fixed batch data, and empirically demonstrate the quality of its behavior in several tasks.

2019-05-23

Proceedings of the 36th International Conference on Machine Learning (published)

proceedings.mlr.press

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)

Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks

Sanjay Thakur

Herke van Hoof

Juan Camilo Gamboa Higuera

Doina Precup

Diversity of environments is a key challenge that causes learned robotic controllers to fail due to the discrepancies between the training a… (see more)nd evaluation conditions. Training from demonstrations in various conditions can mitigate---but not completely prevent---such failures. Learned controllers such as neural networks typically do not have a notion of uncertainty that allows to diagnose an offset between training and testing conditions, and potentially intervene. In this work, we propose to use Bayesian Neural Networks, which have such a notion of uncertainty. We show that uncertainty can be leveraged to consistently detect situations in high-dimensional simulated and real robotic domains in which the performance of the learned controller would be sub-par. Also, we show that such an uncertainty based solution allows making an informed decision about when to invoke a fallback strategy. One fallback strategy is to request more data. We empirically show that providing data only when requested results in increased data-efficiency.

2018-12-31

ICRA (published)