# A3D: Adaptive Affordance Assembly with Dual-Arm Manipulation

arXiv: https://arxiv.org/abs/2601.11076 · HTML: https://mlsanity.com/paper/2601.11076/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 40 references that are in the map.
- First posted on arXiv 16 Jan 2026.

## Builds on (most cited first, 24 of 24)

- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Where2Act: From Pixels to Actions for Articulated 3D Objects](https://mlsanity.com/paper/2101.02692/) (2021)
- [FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation](https://mlsanity.com/paper/2305.12821/) (2023)
- [Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching](https://mlsanity.com/paper/1710.01330/) (2017)
- [EGO-TOPO: Environment Affordances from Egocentric Video](https://mlsanity.com/paper/2001.04583/) (2020)
- [IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks](https://mlsanity.com/paper/1911.07246/) (2019)
- [Generative 3D Part Assembly via Dynamic Graph Learning](https://mlsanity.com/paper/2006.07793/) (2020)
- [Learning Affordance Landscapes for Interaction Exploration in 3D Environments](https://mlsanity.com/paper/2008.09241/) (2020)
- [AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions](https://mlsanity.com/paper/2112.00246/) (2021)
- [Learning 3D Part Assembly from a Single Image](https://mlsanity.com/paper/2003.09754/) (2020)
- [Learning Task-Oriented Grasping from Human Activity Datasets](https://mlsanity.com/paper/1910.11669/) (2019)
- [ET-SEED: Efficient Trajectory-Level SE(3) Equivariant Diffusion Policy](https://mlsanity.com/paper/2411.03990/) (2024)
- [A Modular Robotic Arm Control Stack for Research: Franka-Interface and FrankaPy](https://mlsanity.com/paper/2011.02398/) (2020)
- [Learning Foresightful Dense Visual Affordance for Deformable Object Manipulation](https://mlsanity.com/paper/2303.11057/) (2023)
- [UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondence](https://mlsanity.com/paper/2405.06903/) (2024)
- [Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning](https://mlsanity.com/paper/2203.13733/) (2022)
- [Category-Level Multi-Part Multi-Joint 3D Shape Assembly](https://mlsanity.com/paper/2303.06163/) (2023)
- [RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment](https://mlsanity.com/paper/2112.10143/) (2021)
- [DexGarmentLab: Dexterous Garment Manipulation Environment with Generalizable Policy](https://mlsanity.com/paper/2505.11032/) (2025)
- [Bridging the Sim-to-Real Gap with Dynamic Compliance Tuning for Industrial Insertion](https://mlsanity.com/paper/2311.07499/) (2023)
- [GarmentPile: Point-Level Visual Affordance Guided Retrieval and Adaptation for Cluttered Garments Manipulation](https://mlsanity.com/paper/2503.09243/) (2025)
- [Fabrica: Dual-Arm Assembly of General Multi-Part Objects via Integrated Planning and Learning](https://mlsanity.com/paper/2506.05168/) (2025)
- [BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly](https://mlsanity.com/paper/2506.06221/) (2025)

## Built on by (most cited first, 3 of 3)

- [AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding](https://mlsanity.com/paper/2606.06155/) (2026)
- [Learning Part-Aware Dense 3D Feature Field for Generalizable Articulated Object Manipulation](https://mlsanity.com/paper/2602.14193/) (2026)
- [AssemLM: A Spatial Reasoning Multimodal Large Language Model for Robotic Assembly](https://mlsanity.com/paper/2604.08983/) (2026)

## Abstract (arXiv)

Furniture assembly is a crucial yet challenging task for robots, requiring precise dual-arm coordination where one arm manipulates parts while the other provides collaborative support and stabilization. To accomplish this task more effectively, robots need to actively adapt support strategies throughout the long-horizon assembly process, while also generalizing across diverse part geometries. We propose A3D, a framework which learns adaptive affordances to identify optimal support and stabilization locations on furniture parts. The method employs dense point-level geometric representations to model part interaction patterns, enabling generalization across varied geometries. To handle evolving assembly states, we introduce an adaptive module that uses interaction feedback to dynamically adjust support strategies during assembly based on previous interactions. We establish a simulation environment featuring 50 diverse parts across 8 furniture types, designed for dual-arm collaboration evaluation. Experiments demonstrate that our framework generalizes effectively to diverse part geometries and furniture categories in both simulation and real-world settings.