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Found 8 papers, 2 papers with code
AIGeN: An Adversarial Approach for Instruction Generation in VLN
VLN is a challenging task that involves an agent following human instructions and navigating in a previously unknown environment to reach a specified goal.
Mapping High-level Semantic Regions in Indoor Environments without Object Recognition
Robots require a semantic understanding of their surroundings to operate in an efficient and explainable way in human environments.
Embodied Agents for Efficient Exploration and Smart Scene Description
The development of embodied agents that can communicate with humans in natural language has gained increasing interest over the last years, as it facilitates the diffusion of robotic platforms in human-populated environments.
Embodied Navigation at the Art Gallery
This feature is challenging for occupancy-based agents which are usually trained in crowded domestic environments with plenty of occupancy information.
Spot the Difference: A Novel Task for Embodied Agents in Changing Environments
To make a step towards this setting, we propose Spot the Difference: a novel task for Embodied AI where the agent has access to an outdated map of the environment and needs to recover the correct layout in a fixed time budget.
Focus on Impact: Indoor Exploration with Intrinsic Motivation
The proposed exploration approach outperforms DRL-based competitors relying on intrinsic rewards and surpasses the agents trained with a dense extrinsic reward computed with the environment layouts.
Out of the Box: Embodied Navigation in the Real World
In this work, we detail how to transfer the knowledge acquired in simulation into the real world.
Explore and Explain: Self-supervised Navigation and Recounting
In this paper, we devise a novel embodied setting in which an agent needs to explore a previously unknown environment while recounting what it sees during the path.
