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Object Rearrangement

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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Tru-POMDP: Task Planning Under Uncertainty via Tree of Hypotheses and Open-Ended POMDPs

Embodied Agents Meet Personalization: Exploring Memory Utilization for Personalized Assistance

PARSEC: Preference Adaptation for Robotic Object Rearrangement from Scene Context

LangPert: Detecting and Handling Task-level Perturbations for Robust Object Rearrangement

Embodied Chain of Action Reasoning with Multi-Modal Foundation Model for Humanoid Loco-manipulation

Learning 3D Scene Analogies with Neural Contextual Scene Maps

SoFar: Language-Grounded Orientation Bridges Spatial Reasoning and Object Manipulation

ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement Tasks

Hierarchical Object-Oriented POMDP Planning for Object Rearrangement

Few-Shot Task Learning through Inverse Generative Modeling

3DGS-CD: 3D Gaussian Splatting-based Change Detection for Physical Object Rearrangement

EMOS: Embodiment-aware Heterogeneous Multi-robot Operating System with LLM Agents

M3Bench: Benchmarking Whole-body Motion Generation for Mobile Manipulation in 3D Scenes

Structured Spatial Reasoning with Open Vocabulary Object Detectors

Stimulating Imagination: Towards General-purpose Object Rearrangement

DegustaBot: Zero-Shot Visual Preference Estimation for Personalized Multi-Object Rearrangement

CORE4D: A 4D Human-Object-Human Interaction Dataset for Collaborative Object REarrangement

Reinforcement Learning via Auxiliary Task Distillation

Frankenstein: Generating Semantic-Compositional 3D Scenes in One Tri-Plane

MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting

Grasp, See, and Place: Efficient Unknown Object Rearrangement with Policy Structure Prior

Learning Dual-arm Object Rearrangement for Cartesian Robots

Dream2Real: Zero-Shot 3D Object Rearrangement with Vision-Language Models

STOW: Discrete-Frame Segmentation and Tracking of Unseen Objects for Warehouse Picking Robots

Learning visual-based deformable object rearrangement with local graph neural networks

Video Language Planning

SlotGNN: Unsupervised Discovery of Multi-Object Representations and Visual Dynamics

ORLA*: Mobile Manipulator-Based Object Rearrangement with Lazy A Star

SG-Bot: Object Rearrangement via Coarse-to-Fine Robotic Imagination on Scene Graphs

Multi3DRefer: Grounding Text Description to Multiple 3D Objects

Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning

"Tidy Up the Table": Grounding Common-sense Objective for Tabletop Object Rearrangement

VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models

Multi-Object Manipulation via Object-Centric Neural Scattering Functions

MANER: Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environments

CabiNet: Scaling Neural Collision Detection for Object Rearrangement with Procedural Scene Generation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.