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Found 9 papers, 4 papers with code
Zero Experience Required: Plug & Play Modular Transfer Learning for Semantic Visual Navigation
In reinforcement learning for visual navigation, it is common to develop a model for each new task, and train that model from scratch with task-specific interactions in 3D environments.
Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI
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.
Environment Predictive Coding for Embodied Agents
We introduce environment predictive coding, a self-supervised approach to learn environment-level representations for embodied agents.
Occupancy Anticipation for Efficient Exploration and Navigation
State-of-the-art navigation methods leverage a spatial memory to generalize to new environments, but their occupancy maps are limited to capturing the geometric structures directly observed by the agent.
Ranked #3 on
Robot Navigation
on Habitat 2020 Point Nav test-std
An Exploration of Embodied Visual Exploration
Embodied computer vision considers perception for robots in novel, unstructured environments.
Emergence of Exploratory Look-Around Behaviors through Active Observation Completion
Standard computer vision systems assume access to intelligently captured inputs (e. g., photos from a human photographer), yet autonomously capturing good observations is a major challenge in itself.
Sidekick Policy Learning for Active Visual Exploration
We consider an active visual exploration scenario, where an agent must intelligently select its camera motions to efficiently reconstruct the full environment from only a limited set of narrow field-of-view glimpses.
CoMaL Tracking: Tracking Points at the Object Boundaries
Experiments show improvements over a simple re-detect-and-match framework as well as KLT in terms of speed/accuracy on different real-world applications, especially at the object boundaries.
An Empirical Evaluation of Visual Question Answering for Novel Objects
We study the problem of answering questions about images in the harder setting, where the test questions and corresponding images contain novel objects, which were not queried about in the training data.
