PointGoal Navigation

14 papers with code • 1 benchmarks • 2 datasets

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Use these libraries to find PointGoal Navigation models and implementations

Most implemented papers

Habitat: A Platform for Embodied AI Research

facebookresearch/habitat-sim ICCV 2019

We present Habitat, a platform for research in embodied artificial intelligence (AI).

DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames

facebookresearch/habitat-api ICLR 2020

We leverage this scaling to train an agent for 2. 5 Billion steps of experience (the equivalent of 80 years of human experience) -- over 6 months of GPU-time training in under 3 days of wall-clock time with 64 GPUs.

Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance?

facebookresearch/habitat-api 13 Dec 2019

Second, we investigate the sim2real predictivity of Habitat-Sim for PointGoal navigation.

Learning to Explore using Active Neural SLAM

devendrachaplot/Neural-SLAM ICLR 2020

The use of learning provides flexibility with respect to input modalities (in the SLAM module), leverages structural regularities of the world (in global policies), and provides robustness to errors in state estimation (in local policies).

Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI

facebookresearch/habitat-matterport3d-dataset 16 Sep 2021

When compared to existing photorealistic 3D datasets such as Replica, MP3D, Gibson, and ScanNet, images rendered from HM3D have 20 - 85% higher visual fidelity w. r. t.

Learning to Move with Affordance Maps

wqi/A2L ICLR 2020

In this paper, we combine the best of both worlds with a modular approach that learns a spatial representation of a scene that is trained to be effective when coupled with traditional geometric planners.

Auxiliary Tasks Speed Up Learning PointGoal Navigation

joel99/habitat-pointnav-aux 9 Jul 2020

PointGoal Navigation is an embodied task that requires agents to navigate to a specified point in an unseen environment.

Large Batch Simulation for Deep Reinforcement Learning

shacklettbp/bps-nav ICLR 2021

We accelerate deep reinforcement learning-based training in visually complex 3D environments by two orders of magnitude over prior work, realizing end-to-end training speeds of over 19, 000 frames of experience per second on a single GPU and up to 72, 000 frames per second on a single eight-GPU machine.

Out of the Box: Embodied Navigation in the Real World

aimagelab/LoCoNav 12 May 2021

In this work, we detail how to transfer the knowledge acquired in simulation into the real world.

Realistic PointGoal Navigation via Auxiliary Losses and Information Bottleneck

nicogrande/habitat-pointnav-via-ib 17 Sep 2021

Under this setting, the agent incurs a penalty for using this privileged information, encouraging the agent to only leverage this information when it is crucial to learning.