# FlowBot++: Learning Generalized Articulated Objects Manipulation via Articulation Projection

arXiv: https://arxiv.org/abs/2306.12893 · HTML: https://mlsanity.com/paper/2306.12893/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2023.

## Built on by (most cited first, 30 of 34)

- [Flow as the Cross-Domain Manipulation Interface](https://mlsanity.com/paper/2407.15208/) (2024)
- [General Flow as Foundation Affordance for Scalable Robot Learning](https://mlsanity.com/paper/2401.11439/) (2024)
- [SplatSim: Zero-Shot Sim2Real Transfer of RGB Manipulation Policies Using Gaussian Splatting](https://mlsanity.com/paper/2409.10161/) (2024)
- [NovaFlow: Zero-Shot Manipulation via Actionable Flow from Generated Videos](https://mlsanity.com/paper/2510.08568/) (2025)
- [A3VLM: Actionable Articulation-Aware Vision Language Model](https://mlsanity.com/paper/2406.07549/) (2024)
- [Ag2Manip: Learning Novel Manipulation Skills with Agent-Agnostic Visual and Action Representations](https://mlsanity.com/paper/2404.17521/) (2024)
- [SKIL: Semantic Keypoint Imitation Learning for Generalizable Data-efficient Manipulation](https://mlsanity.com/paper/2501.14400/) (2025)
- [AIC MLLM: Autonomous Interactive Correction MLLM for Robust Robotic Manipulation](https://mlsanity.com/paper/2406.11548/) (2024)
- [Articulated Object Estimation in the Wild](https://mlsanity.com/paper/2509.01708/) (2025)
- [AdaManip: Adaptive Articulated Object Manipulation Environments and Policy Learning](https://mlsanity.com/paper/2502.11124/) (2025)
- [ArticuBot: Learning Universal Articulated Object Manipulation Policy via Large Scale Simulation](https://mlsanity.com/paper/2503.03045/) (2025)
- [Articulated 3D Scene Graphs for Open-World Mobile Manipulation](https://mlsanity.com/paper/2602.16356/) (2026)
- [APLA: Additional Perturbation for Latent Noise with Adversarial Training Enables Consistency](https://mlsanity.com/paper/2308.12605/) (2023)
- [Latent Policy Steering with Embodiment-Agnostic Pretrained World Models](https://mlsanity.com/paper/2507.13340/) (2025)
- [CHAMP: Conformalized 3D Human Multi-Hypothesis Pose Estimators](https://mlsanity.com/paper/2407.06141/) (2024)
- [GAPartManip: A Large-scale Part-centric Dataset for Material-Agnostic Articulated Object Manipulation](https://mlsanity.com/paper/2411.18276/) (2024)
- [AutoURDF: Unsupervised Robot Modeling from Point Cloud Frames Using Cluster Registration](https://mlsanity.com/paper/2412.05507/) (2024)
- [Adaptive Articulated Object Manipulation On The Fly with Foundation Model Reasoning and Part Grounding](https://mlsanity.com/paper/2507.18276/) (2025)
- [Online Estimation and Manipulation of Articulated Objects](https://mlsanity.com/paper/2601.01438/) (2026)
- [Learning Correspondence for Deformable Objects](https://mlsanity.com/paper/2405.08996/) (2024)
- [Safe Deep Model-Based Reinforcement Learning with Lyapunov Functions](https://mlsanity.com/paper/2405.16184/) (2024)
- [CUPS: Improving Human Pose-Shape Estimators with Conformalized Deep Uncertainty](https://mlsanity.com/paper/2412.10431/) (2024)
- [Opening Articulated Structures in the Real World](https://mlsanity.com/paper/2402.17767/) (2024)
- [CenterArt: Joint Shape Reconstruction and 6-DoF Grasp Estimation of Articulated Objects](https://mlsanity.com/paper/2404.14968/) (2024)
- [Learning Sequential Kinematic Models from Demonstrations for Multi-Jointed Articulated Objects](https://mlsanity.com/paper/2505.06363/) (2025)
- [H2OFlow: Grounding Human-Object Affordances with 3D Generative Models and Dense Diffused Flows](https://mlsanity.com/paper/2510.21769/) (2025)
- [Towards Improving Learning from Demonstration Algorithms via MCMC Methods](https://mlsanity.com/paper/2405.02243/) (2024)
- [Theoretical Analysis for Expectation-Maximization-Based Multi-Model 3D Registration](https://mlsanity.com/paper/2405.08991/) (2024)
- [Rocket Landing Control with Grid Fins and Path-following using MPC](https://mlsanity.com/paper/2405.16191/) (2024)
- [ArtReg: Visuo-Tactile based Pose Tracking and Manipulation of Unseen Articulated Objects](https://mlsanity.com/paper/2511.06378/) (2025)

## Abstract (arXiv)

Understanding and manipulating articulated objects, such as doors and drawers, is crucial for robots operating in human environments. We wish to develop a system that can learn to articulate novel objects with no prior interaction, after training on other articulated objects. Previous approaches for articulated object manipulation rely on either modular methods which are brittle or end-to-end methods, which lack generalizability. This paper presents FlowBot++, a deep 3D vision-based robotic system that predicts dense per-point motion and dense articulation parameters of articulated objects to assist in downstream manipulation tasks. FlowBot++ introduces a novel per-point representation of the articulated motion and articulation parameters that are combined to produce a more accurate estimate than either method on their own. Simulated experiments on the PartNet-Mobility dataset validate the performance of our system in articulating a wide range of objects, while real-world experiments on real objects' point clouds and a Sawyer robot demonstrate the generalizability and feasibility of our system in real-world scenarios.