Papers Object Rearrangement
“Object Rearrangement” 태그가 달린 논문 77편 · 필터 해제
CubifyGS: Object-Centric 3D Gaussian Splatting for Lifelong Dynamic Scene Maintenance
Lifelong scene mapping under rigid object rearrangement remains a fundamental challenge in robotics. While 3D Gaussian Splatting (3DGS) enables high-fidelity modeling, primitive-level updates often cause persistent ghost…
Object RearrangementAbstention-Aware Personalized Object Rearrangement via Uncertainty-Guided LLM Assistance
Robotic assistance in household environments requires not only predicting where objects should be placed, but also reasoning about when objects should not be placed at all. Existing approaches to personalized object rear…
Object RearrangementIGV-RRT: Prior-Real-Time Observation Fusion for Active Object Search in Changing Environments
Object Goal Navigation (ObjectNav) in temporally changing indoor environments is challenging because object relocation can invalidate historical scene knowledge. To address this issue, we propose a probabilistic planning…
Object RearrangementDecision MakingEgoPush: Learning End-to-End Egocentric Multi-Object Rearrangement for Mobile Robots
Humans can rearrange objects in cluttered environments using egocentric perception, navigating occlusions without global coordinates. Inspired by this capability, we study long-horizon multi-object non-prehensile rearran…
Object RearrangementPush-Placement: A Hybrid Approach Integrating Prehensile and Non-Prehensile Manipulation for Object Rearrangement
Efficient tabletop rearrangement remains challenging due to collisions and the need for temporary buffering when target poses are obstructed. Prehensile pick-and-place provides precise control but often requires extra mo…
Object RearrangementALORE: Autonomous Large-Object Rearrangement with a Legged Manipulator
Endowing robots with the ability to rearrange various large and heavy objects, such as furniture, can substantially alleviate human workload. However, this task is extremely challenging due to the need to interact with d…
Hierarchical Reinforcement LearningObject RearrangementMotion PlanningMulti-Agent Monte Carlo Tree Search for Makespan-Efficient Object Rearrangement in Cluttered Spaces
Object rearrangement planning in complex, cluttered environments is a common challenge in warehouses, households, and rescue sites. Prior studies largely address monotone instances, whereas real-world tasks are often non…
Object RearrangementReloPush-BOSS: Optimization-guided Nonmonotone Rearrangement Planning for a Car-like Robot Pusher
We focus on multi-object rearrangement planning in densely cluttered environments using a car-like robot pusher. The combination of kinematic, geometric and physics constraints underlying this domain results in challengi…
Object RearrangementExplaining Why Things Go Where They Go: Interpretable Constructs of Human Organizational Preferences
Robotic systems for household object rearrangement often rely on latent preference models inferred from human demonstrations. While effective at prediction, these models offer limited insight into the interpretable facto…
Object RearrangementManualVLA: A Unified VLA Model for Chain-of-Thought Manual Generation and Robotic Manipulation
Vision-Language-Action (VLA) models have recently emerged, demonstrating strong generalization in robotic scene understanding and manipulation. However, when confronted with long-horizon tasks that require defined goal s…
Object RearrangementScene UnderstandingLLM-GROP: Visually Grounded Robot Task and Motion Planning with Large Language Models
Task planning and motion planning are two of the most important problems in robotics, where task planning methods help robots achieve high-level goals and motion planning methods maintain low-level feasibility. Task and …
Object RearrangementMotion PlanningImagine2Act: Leveraging Object-Action Motion Consistency from Imagined Goals for Robotic Manipulation
Relational object rearrangement (ROR) tasks (e.g., insert flower to vase) require a robot to manipulate objects with precise semantic and geometric reasoning. Existing approaches either rely on pre-collected demonstratio…
Object RearrangementPoint CloudsPlanning from Point Clouds over Continuous Actions for Multi-object Rearrangement
Long-horizon planning for robot manipulation is a challenging problem that requires reasoning about the effects of a sequence of actions on a physical 3D scene. While traditional task planning methods are shown to be eff…
Object RearrangementRobot ManipulationPoint CloudsIGFuse: Interactive 3D Gaussian Scene Reconstruction via Multi-Scans Fusion
Reconstructing complete and interactive 3D scenes remains a fundamental challenge in computer vision and robotics, particularly due to persistent object occlusions and limited sensor coverage. Multiview observations from…
Object Rearrangement3D ReconstructionTru-POMDP: Task Planning Under Uncertainty via Tree of Hypotheses and Open-Ended POMDPs
Task planning under uncertainty is essential for home-service robots operating in the real world. Tasks involve ambiguous human instructions, hidden or unknown object locations, and open-vocabulary object types, leading …
ObjectObject RearrangementTask PlanningEmbodied Agents Meet Personalization: Exploring Memory Utilization for Personalized Assistance
Embodied agents empowered by large language models (LLMs) have shown strong performance in household object rearrangement tasks. However, these tasks primarily focus on single-turn interactions with simplified instructio…
ObjectObject RearrangementPARSEC: Preference Adaptation for Robotic Object Rearrangement from Scene Context
Object rearrangement is a key task for household robots requiring personalization without explicit instructions, meaningful object placement in environments occupied with objects, and generalization to unseen objects and…
ObjectObject RearrangementLangPert: Detecting and Handling Task-level Perturbations for Robust Object Rearrangement
Task execution for object rearrangement could be challenged by Task-Level Perturbations (TLP), i.e., unexpected object additions, removals, and displacements that can disrupt underlying visual policies and fundamentally …
Language ModelingLanguage ModellingLarge Language ModelObject RearrangementEmbodied Chain of Action Reasoning with Multi-Modal Foundation Model for Humanoid Loco-manipulation
Enabling humanoid robots to autonomously perform loco-manipulation tasks in complex, unstructured environments poses significant challenges. This entails equipping robots with the capability to plan actions over extended…
NavigateObject RearrangementSpatial ReasoningLearning 3D Scene Analogies with Neural Contextual Scene Maps
Understanding scene contexts is crucial for machines to perform tasks and adapt prior knowledge in unseen or noisy 3D environments. As data-driven learning is intractable to comprehensively encapsulate diverse ranges of …
Imitation LearningObject Rearrangement