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no code implementations • 7 Mar 2022 • Miguel Saavedra-Ruiz, Sacha Morin, Liam Paull

In this work, we consider the problem of learning a perception model for monocular robot navigation using few annotated images.

1 code implementation • ICLR 2022 • Vincent Mai, Kaustubh Mani, Liam Paull

In model-free deep reinforcement learning (RL) algorithms, using noisy value estimates to supervise policy evaluation and optimization is detrimental to the sample efficiency.

no code implementations • NeurIPS 2021 • Weiyang Liu, Zhen Liu, Hanchen Wang, Liam Paull, Bernhard Schölkopf, Adrian Weller

In this paper, we consider the problem of iterative machine teaching, where a teacher provides examples sequentially based on the current iterative learner.

no code implementations • 16 Oct 2021 • Rey Reza Wiyatno, Anqi Xu, Liam Paull

However, while end-to-end methods tend to struggle in long-distance navigation tasks, topological map-based solutions are prone to failure due to spurious edges in the graph.

1 code implementation • 29 Sep 2021 • Anthony Courchesne, Andrea Censi, Liam Paull

We propose the relative predictive PU to assess the predictive ability of a proxy domain and the learning PU to quantify the usefulness of a proxy as a tool to generate learning data.

no code implementations • 28 Sep 2021 • Dhaivat Bhatt, Kaustubh Mani, Dishank Bansal, Krishna Murthy, Hanju Lee, Liam Paull

While modern deep neural networks are performant perception modules, performance (accuracy) alone is insufficient, particularly for safety-critical robotic applications such as self-driving vehicles.

no code implementations • 9 Jul 2021 • Vincent Mai, Waleed Khamies, Liam Paull

In many situations however, the labelling process is able to estimate the variance of such distribution for each label, which can be used as an additional information to mitigate this impact.

no code implementations • ICLR 2021 • Krishna Murthy Jatavallabhula, Miles Macklin, Florian Golemo, Vikram Voleti, Linda Petrini, Martin Weiss, Breandan Considine, Jerome Parent-Levesque, Kevin Xie, Kenny Erleben, Liam Paull, Florian Shkurti, Derek Nowrouzezahrai, Sanja Fidler

We consider the problem of estimating an object's physical properties such as mass, friction, and elasticity directly from video sequences.

no code implementations • ICLR Workshop SSL-RL 2021 • Manfred Diaz, Liam Paull, Pablo Samuel Castro

We offer a novel approach to balance exploration and exploitation in reinforcement learning (RL).

no code implementations • 1 Jan 2021 • Vincent Mai, Waleed Khamies, Liam Paull

In this work, we consider this setting and additionally assume that the label generating process is able to provide us with a quantity for the role of each label in the misalignment between the datasets, which we consider to be privileged information.

2 code implementations • NeurIPS 2020 • Gunshi Gupta, Karmesh Yadav, Liam Paull

The continual learning problem involves training models with limited capacity to perform well on a set of an unknown number of sequentially arriving tasks.

no code implementations • 9 Sep 2020 • Jacopo Tani, Andrea F. Daniele, Gianmarco Bernasconi, Amaury Camus, Aleksandar Petrov, Anthony Courchesne, Bhairav Mehta, Rohit Suri, Tomasz Zaluska, Matthew R. Walter, Emilio Frazzoli, Liam Paull, Andrea Censi

As robotics matures and increases in complexity, it is more necessary than ever that robot autonomy research be reproducible.

3 code implementations • ICML Workshop LifelongML 2020 • Gunshi Gupta, Karmesh Yadav, Liam Paull

The continual learning problem involves training models with limited capacity to perform well on a set of an unknown number of sequentially arriving tasks.

1 code implementation • CVPR 2021 • Weiyang Liu, Rongmei Lin, Zhen Liu, James M. Rehg, Liam Paull, Li Xiong, Le Song, Adrian Weller

The inductive bias of a neural network is largely determined by the architecture and the training algorithm.

3 code implementations • NeurIPS 2020 • Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, Yoshua Bengio

To make that practical, we show that sampling from this modified density can be achieved by sampling in latent space according to an energy-based model induced by the sum of the latent prior log-density and the discriminator output score.

no code implementations • 19 Feb 2020 • Bhairav Mehta, Tristan Deleu, Sharath Chandra Raparthy, Chris J. Pal, Liam Paull

However, specifically in the case of meta-reinforcement learning (meta-RL), we can show that gradient-based meta-learners are sensitive to task distributions.

1 code implementation • 18 Feb 2020 • Sharath Chandra Raparthy, Bhairav Mehta, Florian Golemo, Liam Paull

Goal-directed Reinforcement Learning (RL) traditionally considers an agent interacting with an environment, prescribing a real-valued reward to an agent proportional to the completion of some goal.

1 code implementation • 23 Oct 2019 • Krishna Murthy Jatavallabhula, Soroush Saryazdi, Ganesh Iyer, Liam Paull

Blending representation learning approaches with simultaneous localization and mapping (SLAM) systems is an open question, because of their highly modular and complex nature.

Representation Learning Simultaneous Localization and Mapping

2 code implementations • ICML 2020 • Zijun Zhang, Ruixiang Zhang, Zongpeng Li, Yoshua Bengio, Liam Paull

We therefore propose to map both the generated and target distributions to a latent space using the encoder of a standard autoencoder, and train the generator (or decoder) to match the target distribution in the latent space.

1 code implementation • 9 Apr 2019 • Bhairav Mehta, Manfred Diaz, Florian Golemo, Christopher J. Pal, Liam Paull

Our experiments show that domain randomization may lead to suboptimal, high-variance policies, which we attribute to the uniform sampling of environment parameters.

1 code implementation • 5 Mar 2019 • Sai Krishna, Keehong Seo, Dhaivat Bhatt, Vincent Mai, Krishna Murthy, Liam Paull

Traditional approaches to this use an information-theoretic criterion for action selection and hand-crafted perceptual models.

no code implementations • 11 Oct 2018 • Homanga Bharadhwaj, Zihan Wang, Yoshua Bengio, Liam Paull

Learning effective visuomotor policies for robots purely from data is challenging, but also appealing since a learning-based system should not require manual tuning or calibration.

no code implementations • 11 Apr 2018 • Ganesh Iyer, J. Krishna Murthy, Gunshi Gupta, K. Madhava Krishna, Liam Paull

We show that using a noisy teacher, which could be a standard VO pipeline, and by designing a loss term that enforces geometric consistency of the trajectory, we can train accurate deep models for VO that do not require ground-truth labels.

2 code implementations • 19 Apr 2017 • Beipeng Mu, Shih-Yuan Liu, Liam Paull, John Leonard, Jonathan How

The \textit{data association} and \textit{simultaneous localization and mapping} (SLAM) problems are, individually, well-studied in the literature.

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