Rearrangement Planning for General Part Assembly

2023-07-01 · Yulong Li, Andy Zeng, Shuran Song

Most successes in autonomous robotic assembly have been restricted to single target or category. We propose to investigate general part assembly, the task of creating novel target assemblies with unseen part shapes. As a fundamental step to a general part assembly system, we tackle the task of determining the precise poses of the parts in the target assembly, which we we term ``rearrangement planning''. We present General Part Assembly Transformer (GPAT), a transformer-based model architecture that accurately predicts part poses by inferring how each part shape corresponds to the target shape. Our experiments on both 3D CAD models and real-world scans demonstrate GPAT's generalization abilities to novel and diverse target and part shapes.

📄 PDF Abstract BibTeX arXiv:2307.00206

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

Multi-Head Attention 설명 없음

Attention 설명 없음

Layer Normalization Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs…

Absolute Position Encodings Absolute Position Encodings are a type of position embeddings for [Transformer-based models] where positional encodings are…

BPE Byte Pair Encoding, or BPE, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word…

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

Label Smoothing Label Smoothing is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the…

Adam 설명 없음

ManualVLA: A Unified VLA Model for Chain-of-Thought Manual Generation and Robotic Manipulation

2025-12-01 · Chenyang Gu, Jiaming Liu, Hao Chen, Runzhong Huang 외 arxiv

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 Rearrangement Scene Understanding

Effective Baselines for Multiple Object Rearrangement Planning in Partially Observable Mapped Environments

2023-01-24 · Engin Tekin, Elaheh Barati, Nitin Kamra, Ruta Desai

Many real-world tasks, from house-cleaning to cooking, can be formulated as multi-object rearrangement problems -- where an agent needs to get specific objects into appropriate goal states. For such problems, we focus on…

Deep Reinforcement Learning Object Object Rearrangement Object Recognition

NeRP: Neural Rearrangement Planning for Unknown Objects

2021-06-02 · Ahmed H. Qureshi, Arsalan Mousavian, Chris Paxton, Michael C. Yip 외

Robots will be expected to manipulate a wide variety of objects in complex and arbitrary ways as they become more widely used in human environments. As such, the rearrangement of objects has been noted to be an important…

Object Rearrangement

Hierarchical Object-Oriented POMDP Planning for Object Rearrangement

2024-12-02 · Rajesh Mangannavar, Alan Fern, Prasad Tadepalli

We present an online planning framework for solving multi-object rearrangement problems in partially observable, multi-room environments. Current object rearrangement solutions, primarily based on Reinforcement Learning …

Object Object Rearrangement

Embodied Scene Rearrangement Planning

2026-08-27 · Canzhi Chen, Zan Wang, Siqi Zhu, Qi Wu 외 arxiv

This paper introduces Embodied Scene Rearrangement Planning (ESRP), a novel task requiring embodied agents to rearrange furniture in 3D scenes to match a target configuration using only egocentric observations and a top-…

Scene Understanding Motion Planning