# Part-Guided 3D RL for Sim2Real Articulated Object Manipulation

arXiv: https://arxiv.org/abs/2404.17302 · HTML: https://mlsanity.com/paper/2404.17302/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 38 references that are in the map.
- First posted on arXiv 26 Apr 2024.

## Builds on (most cited first, 30 of 31)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Deep Hough Voting for 3D Object Detection in Point Clouds](https://mlsanity.com/paper/1904.09664/) (2019)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels](https://mlsanity.com/paper/2004.13649/) (2020)
- [VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training](https://mlsanity.com/paper/2210.00030/) (2022)
- [Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks](https://mlsanity.com/paper/1807.07356/) (2018)
- [Masked Visual Pre-training for Motor Control](https://mlsanity.com/paper/2203.06173/) (2022)
- [Real-World Robot Learning with Masked Visual Pre-training](https://mlsanity.com/paper/2210.03109/) (2022)
- [Where2Act: From Pixels to Actions for Articulated 3D Objects](https://mlsanity.com/paper/2101.02692/) (2021)
- [Ditto: Building Digital Twins of Articulated Objects from Interaction](https://mlsanity.com/paper/2202.08227/) (2022)
- [GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts](https://mlsanity.com/paper/2211.05272/) (2022)
- [Reinforcement Learning with Action-Free Pre-Training from Videos](https://mlsanity.com/paper/2203.13880/) (2022)
- [FlowBot3D: Learning 3D Articulation Flow to Manipulate Articulated Objects](https://mlsanity.com/paper/2205.04382/) (2022)
- [DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation](https://mlsanity.com/paper/2211.09423/) (2022)
- [VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects](https://mlsanity.com/paper/2106.14440/) (2021)
- [UMPNet: Universal Manipulation Policy Network for Articulated Objects](https://mlsanity.com/paper/2109.05668/) (2021)
- [AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions](https://mlsanity.com/paper/2112.00246/) (2021)
- [End-to-End Affordance Learning for Robotic Manipulation](https://mlsanity.com/paper/2209.12941/) (2022)
- [S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenes](https://mlsanity.com/paper/1910.14218/) (2019)
- [Learning to See before Learning to Act: Visual Pre-training for Manipulation](https://mlsanity.com/paper/2107.00646/) (2021)
- [Vision-Based Manipulators Need to Also See from Their Hands](https://mlsanity.com/paper/2203.12677/) (2022)
- [Frame Mining: a Free Lunch for Learning Robotic Manipulation from 3D Point Clouds](https://mlsanity.com/paper/2210.07442/) (2022)
- [DoorGym: A Scalable Door Opening Environment And Baseline Agent](https://mlsanity.com/paper/1908.01887/) (2019)
- [Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation](https://mlsanity.com/paper/2012.07975/) (2020)
- [Close the Optical Sensing Domain Gap by Physics-Grounded Active Stereo Sensor Simulation](https://mlsanity.com/paper/2201.11924/) (2022)

## Built on by (most cited first, 9 of 9)

- [PartNeXt: A Next-Generation Dataset for Fine-Grained and Hierarchical 3D Part Understanding](https://mlsanity.com/paper/2510.20155/) (2025)
- [Diffusion-Based Depth Inpainting for Transparent and Reflective Objects](https://mlsanity.com/paper/2410.08567/) (2024)
- [MATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policies](https://mlsanity.com/paper/2409.15517/) (2024)
- [M3Bench: Benchmarking Whole-body Motion Generation for Mobile Manipulation in 3D Scenes](https://mlsanity.com/paper/2410.06678/) (2024)
- [Energy-Aware Reinforcement Learning for Robotic Manipulation of Articulated Components in Infrastructure Operation and Maintenance](https://mlsanity.com/paper/2602.12288/) (2026)
- [FLEX: A Framework for Learning Robot-Agnostic Force-based Skills Involving Sustained Contact Object Manipulation](https://mlsanity.com/paper/2503.13418/) (2025)
- [Efficient 3D Perception on Multi-Sweep Point Cloud with Gumbel Spatial Pruning](https://mlsanity.com/paper/2411.07742/) (2024)
- [Data-Efficient Multitask DAgger](https://mlsanity.com/paper/2509.25466/) (2025)
- [ArtReg: Visuo-Tactile based Pose Tracking and Manipulation of Unseen Articulated Objects](https://mlsanity.com/paper/2511.06378/) (2025)

## Abstract (arXiv)

Manipulating unseen articulated objects through visual feedback is a critical but challenging task for real robots. Existing learning-based solutions mainly focus on visual affordance learning or other pre-trained visual models to guide manipulation policies, which face challenges for novel instances in real-world scenarios. In this paper, we propose a novel part-guided 3D RL framework, which can learn to manipulate articulated objects without demonstrations. We combine the strengths of 2D segmentation and 3D RL to improve the efficiency of RL policy training. To improve the stability of the policy on real robots, we design a Frame-consistent Uncertainty-aware Sampling (FUS) strategy to get a condensed and hierarchical 3D representation. In addition, a single versatile RL policy can be trained on multiple articulated object manipulation tasks simultaneously in simulation and shows great generalizability to novel categories and instances. Experimental results demonstrate the effectiveness of our framework in both simulation and real-world settings. Our code is available at https://github.com/THU-VCLab/Part-Guided-3D-RL-for-Sim2Real-Articulated-Object-Manipulation.