Search Results for author: Oleksandr Maksymets

Found 17 papers, 9 papers with code

OVRL-V2: A simple state-of-art baseline for ImageNav and ObjectNav

no code implementations14 Mar 2023 Karmesh Yadav, Arjun Majumdar, Ram Ramrakhya, Naoki Yokoyama, Alexei Baevski, Zsolt Kira, Oleksandr Maksymets, Dhruv Batra

We present a single neural network architecture composed of task-agnostic components (ViTs, convolutions, and LSTMs) that achieves state-of-art results on both the ImageNav ("go to location in <this picture>") and ObjectNav ("find a chair") tasks without any task-specific modules like object detection, segmentation, mapping, or planning modules.

object-detection Object Detection +3

Is Mapping Necessary for Realistic PointGoal Navigation?

1 code implementation CVPR 2022 Ruslan Partsey, Erik Wijmans, Naoki Yokoyama, Oles Dobosevych, Dhruv Batra, Oleksandr Maksymets

However, for PointNav in a realistic setting (RGB-D and actuation noise, no GPS+Compass), this is an open question; one we tackle in this paper.

Data Augmentation Navigate +3

Offline Visual Representation Learning for Embodied Navigation

2 code implementations27 Apr 2022 Karmesh Yadav, Ram Ramrakhya, Arjun Majumdar, Vincent-Pierre Berges, Sachit Kuhar, Dhruv Batra, Alexei Baevski, Oleksandr Maksymets

In this paper, we show that an alternative 2-stage strategy is far more effective: (1) offline pretraining of visual representations with self-supervised learning (SSL) using large-scale pre-rendered images of indoor environments (Omnidata), and (2) online finetuning of visuomotor representations on specific tasks with image augmentations under long learning schedules.

Representation Learning Self-Supervised Learning

Waypoint Models for Instruction-guided Navigation in Continuous Environments

1 code implementation ICCV 2021 Jacob Krantz, Aaron Gokaslan, Dhruv Batra, Stefan Lee, Oleksandr Maksymets

Little inquiry has explicitly addressed the role of action spaces in language-guided visual navigation -- either in terms of its effect on navigation success or the efficiency with which a robotic agent could execute the resulting trajectory.

Instruction Following Visual Navigation

THDA: Treasure Hunt Data Augmentation for Semantic Navigation

no code implementations ICCV 2021 Oleksandr Maksymets, Vincent Cartillier, Aaron Gokaslan, Erik Wijmans, Wojciech Galuba, Stefan Lee, Dhruv Batra

We show that this is a natural consequence of optimizing for the task metric (which in fact penalizes exploration), is enabled by powerful observation encoders, and is possible due to the finite set of training environment configurations.

Data Augmentation Navigate +3

Integrating Egocentric Localization for More Realistic Point-Goal Navigation Agents

no code implementations7 Sep 2020 Samyak Datta, Oleksandr Maksymets, Judy Hoffman, Stefan Lee, Dhruv Batra, Devi Parikh

This enables a seamless adaption to changing dynamics (a different robot or floor type) by simply re-calibrating the visual odometry model -- circumventing the expense of re-training of the navigation policy.

Navigate Robot Navigation +1

ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects

3 code implementations23 Jun 2020 Dhruv Batra, Aaron Gokaslan, Aniruddha Kembhavi, Oleksandr Maksymets, Roozbeh Mottaghi, Manolis Savva, Alexander Toshev, Erik Wijmans

In particular, the agent is initialized at a random location and pose in an environment and asked to find an instance of an object category, e. g., find a chair, by navigating to it.


Embodied Question Answering in Photorealistic Environments with Point Cloud Perception

no code implementations CVPR 2019 Erik Wijmans, Samyak Datta, Oleksandr Maksymets, Abhishek Das, Georgia Gkioxari, Stefan Lee, Irfan Essa, Devi Parikh, Dhruv Batra

To help bridge the gap between internet vision-style problems and the goal of vision for embodied perception we instantiate a large-scale navigation task -- Embodied Question Answering [1] in photo-realistic environments (Matterport 3D).

Embodied Question Answering Question Answering

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