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Found 6 papers, 6 papers with code
Language Conditioned Spatial Relation Reasoning for 3D Object Grounding
In this work we propose a language-conditioned transformer model for grounding 3D objects and their spatial relations.
Instruction-driven history-aware policies for robotic manipulations
In human environments, robots are expected to accomplish a variety of manipulation tasks given simple natural language instructions.
Ranked #2 on
Robot Manipulation
on RLBench
(Succ. Rate (10 tasks, 100 demos/task) metric)
Learning from Unlabeled 3D Environments for Vision-and-Language Navigation
Our resulting HM3D-AutoVLN dataset is an order of magnitude larger than existing VLN datasets in terms of navigation environments and instructions.
Ranked #1 on
Visual Navigation
on SOON Test
Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language Navigation
To balance the complexity of large action space reasoning and fine-grained language grounding, we dynamically combine a fine-scale encoding over local observations and a coarse-scale encoding on a global map via graph transformers.
Ranked #4 on
Visual Navigation
on SOON Test
History Aware Multimodal Transformer for Vision-and-Language Navigation
Vision-and-language navigation (VLN) aims to build autonomous visual agents that follow instructions and navigate in real scenes.
Ranked #3 on
Vision and Language Navigation
on RxR
Airbert: In-domain Pretraining for Vision-and-Language Navigation
Given the scarcity of domain-specific training data and the high diversity of image and language inputs, the generalization of VLN agents to unseen environments remains challenging.
Ranked #3 on
Vision and Language Navigation
on VLN Challenge
