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Found 11 papers, 4 papers with code
QueSTMaps: Queryable Semantic Topological Maps for 3D Scene Understanding
Understanding the structural organisation of 3D indoor scenes in terms of rooms is often accomplished via floorplan extraction.
Anticipate & Collab: Data-driven Task Anticipation and Knowledge-driven Planning for Human-robot Collaboration
Specifically, DaTAPlan planner computes actions for an agent and a human to collaboratively and jointly achieve the tasks anticipated by the LLM, and the agent automatically adapts to unexpected changes in human action outcomes and preferences.
LIP-Loc: LiDAR Image Pretraining for Cross-Modal Localization
We apply this approach to the domains of 2D image and 3D LiDAR points on the task of cross-modal localization.
EDMP: Ensemble-of-costs-guided Diffusion for Motion Planning
However, without a prior understanding of what diverse valid trajectories are and without specially designed cost functions for a given scene, the overall solutions tend to have low success rates.
AnyLoc: Towards Universal Visual Place Recognition
In this work, we develop a universal solution to VPR -- a technique that works across a broad range of structured and unstructured environments (urban, outdoors, indoors, aerial, underwater, and subterranean environments) without any re-training or fine-tuning.
Ranked #1 on
Visual Place Recognition
on Nardo-Air R
Sequence-Agnostic Multi-Object Navigation
The Multi-Object Navigation (MultiON) task requires a robot to localize an instance (each) of multiple object classes.
ConceptFusion: Open-set Multimodal 3D Mapping
ConceptFusion leverages the open-set capabilities of today's foundation models pre-trained on internet-scale data to reason about concepts across modalities such as natural language, images, and audio.
SCARP: 3D Shape Completion in ARbitrary Poses for Improved Grasping
Unlike existing methods that depend on an external canonicalization, SCARP performs canonicalization, pose estimation, and shape completion in a single network, improving the performance by 45% over the existing baselines.
Canonical Fields: Self-Supervised Learning of Pose-Canonicalized Neural Fields
We present Canonical Field Network (CaFi-Net), a self-supervised method to canonicalize the 3D pose of instances from an object category represented as neural fields, specifically neural radiance fields (NeRFs).
Spatial Relation Graph and Graph Convolutional Network for Object Goal Navigation
This paper describes a framework for the object-goal navigation task, which requires a robot to find and move to the closest instance of a target object class from a random starting position.
Object Goal Navigation using Data Regularized Q-Learning
Our framework incrementally builds a semantic map of the environment over time, and then repeatedly selects a long-term goal ('where to go') based on the semantic map to locate the target object instance.
