Papers PointGoal Navigation
“PointGoal Navigation” 태그가 달린 논문 27편 · 필터 해제
Robust Scene Transfer for PointGoal Navigation via Privileged Sensor Guided Contrastive Learning
We propose a sensor-guided adaptive contrastive learning framework for visual representation learning in PointGoal navigation. During training, privileged LiDAR sensing guides the contrastive objective through a geometry…
Representation LearningReinforcement LearningContrastive LearningPointGoal NavigationIndustryNav: Exploring Spatial Reasoning of Embodied Agents in Dynamic Industrial Navigation
While Visual Large Language Models (VLLMs) show great promise as embodied agents, they continue to face substantial challenges in spatial reasoning. Existing embodied benchmarks largely focus on passive, static household…
PointGoal NavigationCollision AvoidanceSpatial ReasoningTANGO: Training-free Embodied AI Agents for Open-world Tasks
Large Language Models (LLMs) have demonstrated excellent capabilities in composing various modules together to create programs that can perform complex reasoning tasks on images. In this paper, we propose TANGO, an appro…
Embodied Question AnsweringObjectGoal NavigationPointGoal NavigationQuestion AnsweringMPVO: Motion-Prior based Visual Odometry for PointGoal Navigation
Visual odometry (VO) is essential for enabling accurate point-goal navigation of embodied agents in indoor environments where GPS and compass sensors are unreliable and inaccurate. However, traditional VO methods face ch…
PointGoal NavigationVisual OdometryMOPA: Modular Object Navigation with PointGoal Agents
We propose a simple but effective modular approach MOPA (Modular ObjectNav with PointGoal agents) to systematically investigate the inherent modularity of the object navigation task in Embodied AI. MOPA consists of four …
NavigateObjectobject-detectionObject Detection+1Emergence of Maps in the Memories of Blind Navigation Agents
Animal navigation research posits that organisms build and maintain internal spatial representations, or maps, of their environment. We ask if machines -- specifically, artificial intelligence (AI) navigation agents -- a…
Inductive BiasPointGoal NavigationComparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal Navigation
In recent years several learning approaches to point goal navigation in previously unseen environments have been proposed. They vary in the representations of the environments, problem decomposition, and experimental eva…
Deep Reinforcement LearningmodelPointGoal NavigationProblem Decomposition+4VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement
We present Variable Experience Rollout (VER), a technique for efficiently scaling batched on-policy reinforcement learning in heterogenous environments (where different environments take vastly different times to generat…
GPUNavigateObjectGoal NavigationOut-of-Distribution Generalization+1Unsupervised Visual Odometry and Action Integration for PointGoal Navigation in Indoor Environment
PointGoal navigation in indoor environment is a fundamental task for personal robots to navigate to a specified point. Recent studies solved this PointGoal navigation task with near-perfect success rate in photo-realisti…
NavigatePointGoal NavigationVisual OdometryIs Mapping Necessary for Realistic PointGoal Navigation?
Can an autonomous agent navigate in a new environment without building an explicit map? For the task of PointGoal navigation ('Go to $\Delta x$, $\Delta y$') under idealized settings (no RGB-D and actuation noise, perfec…
Data AugmentationNavigateOpen-Ended Question AnsweringPointGoal Navigation+1Embodied Navigation at the Art Gallery
Embodied agents, trained to explore and navigate indoor photorealistic environments, have achieved impressive results on standard datasets and benchmarks. So far, experiments and evaluations have involved domestic and wo…
NavigatePointGoal NavigationImage-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models, Benchmark and Efficient Evaluation
Navigating complex indoor environments requires a deep understanding of the space the robotic agent is acting into to correctly inform the navigation process of the agent towards the goal location. In recent learning-bas…
PointGoal NavigationScene UnderstandingBenchmarking Augmentation Methods for Learning Robust Navigation Agents: the Winning Entry of the 2021 iGibson Challenge
Recent advances in deep reinforcement learning and scalable photorealistic simulation have led to increasingly mature embodied AI for various visual tasks, including navigation. However, while impressive progress has bee…
BenchmarkingData AugmentationDeep Reinforcement LearningImage Augmentation+4Realistic PointGoal Navigation via Auxiliary Losses and Information Bottleneck
We propose a novel architecture and training paradigm for training realistic PointGoal Navigation -- navigating to a target coordinate in an unseen environment under actuation and sensor noise without access to ground-tr…
PointGoal NavigationHabitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI
We present the Habitat-Matterport 3D (HM3D) dataset. HM3D is a large-scale dataset of 1,000 building-scale 3D reconstructions from a diverse set of real-world locations. Each scene in the dataset consists of a textured 3…
PointGoal NavigationSurface ReconstructionThe Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation
It is fundamental for personal robots to reliably navigate to a specified goal. To study this task, PointGoal navigation has been introduced in simulated Embodied AI environments. Recent advances solve this PointGoal nav…
NavigatePointGoal NavigationVisual OdometryOut of the Box: Embodied Navigation in the Real World
The research field of Embodied AI has witnessed substantial progress in visual navigation and exploration thanks to powerful simulating platforms and the availability of 3D data of indoor and photorealistic environments.…
PointGoal NavigationVisual NavigationGridToPix: Training Embodied Agents with Minimal Supervision
While deep reinforcement learning (RL) promises freedom from hand-labeled data, great successes, especially for Embodied AI, require significant work to create supervision via carefully shaped rewards. Indeed, without sh…
Deep Reinforcement LearningPointGoal NavigationReinforcement Learning (RL)Task 2Large Batch Simulation for Deep Reinforcement Learning
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…
Deep Reinforcement LearningGPUPointGoal Navigationreinforcement-learning+2THDA: Treasure Hunt Data Augmentation for Semantic Navigation
Can general-purpose neural models learn to navigate? For PointGoal navigation (""go to x, y""), the answer is a clear `yes' -- mapless neural models composed of task-agnostic components (CNNs and RNNs) trained with l…
Data AugmentationNavigateObjectGoal NavigationOpen-Ended Question Answering+1