# AnyPlace: Learning Generalized Object Placement for Robot Manipulation

arXiv: https://arxiv.org/abs/2502.04531 · HTML: https://mlsanity.com/paper/2502.04531/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 27 references that are in the map.
- First posted on arXiv 6 Feb 2025.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities](https://mlsanity.com/paper/2401.12168/) (2024)
- [Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments](https://mlsanity.com/paper/2301.04195/) (2023)
- [RVT: Robotic View Transformer for 3D Object Manipulation](https://mlsanity.com/paper/2306.14896/) (2023)
- [Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation](https://mlsanity.com/paper/2112.05124/) (2021)
- [AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains](https://mlsanity.com/paper/2212.08333/) (2022)
- [RVT-2: Learning Precise Manipulation from Few Demonstrations](https://mlsanity.com/paper/2406.08545/) (2024)
- [Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation](https://mlsanity.com/paper/2106.12534/) (2021)
- [Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold](https://mlsanity.com/paper/2106.05965/) (2021)
- [Equivariant Descriptor Fields: SE(3)-Equivariant Energy-Based Models for End-to-End Visual Robotic Manipulation Learning](https://mlsanity.com/paper/2206.08321/) (2022)
- [Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation](https://mlsanity.com/paper/2309.02685/) (2023)
- [SE(3)-Equivariant Relational Rearrangement with Neural Descriptor Fields](https://mlsanity.com/paper/2211.09786/) (2022)
- [TAX-Pose: Task-Specific Cross-Pose Estimation for Robot Manipulation](https://mlsanity.com/paper/2211.09325/) (2022)
- [Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors](https://mlsanity.com/paper/2205.14886/) (2022)
- [M2T2: Multi-Task Masked Transformer for Object-centric Pick and Place](https://mlsanity.com/paper/2311.00926/) (2023)
- [Shelving, Stacking, Hanging: Relational Pose Diffusion for Multi-modal Rearrangement](https://mlsanity.com/paper/2307.04751/) (2023)
- [Local Neural Descriptor Fields: Locally Conditioned Object Representations for Manipulation](https://mlsanity.com/paper/2302.03573/) (2023)
- [Deep SE(3)-Equivariant Geometric Reasoning for Precise Placement Tasks](https://mlsanity.com/paper/2404.13478/) (2024)
- [Imagination Policy: Using Generative Point Cloud Models for Learning Manipulation Policies](https://mlsanity.com/paper/2406.11740/) (2024)
- [RiEMann: Near Real-Time SE(3)-Equivariant Robot Manipulation without Point Cloud Segmentation](https://mlsanity.com/paper/2403.19460/) (2024)
- [OmniHang: Learning to Hang Arbitrary Objects using Contact Point Correspondences and Neural Collision Estimation](https://mlsanity.com/paper/2103.14283/) (2021)
- [MATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policies](https://mlsanity.com/paper/2409.15517/) (2024)
- [Pick2Place: Task-aware 6DoF Grasp Estimation via Object-Centric Perspective Affordance](https://mlsanity.com/paper/2304.04100/) (2023)

## Built on by (most cited first, 11 of 11)

- [Diffusion Models for Robotic Manipulation: A Survey](https://mlsanity.com/paper/2504.08438/) (2025)
- [Seeing to Act, Prompting to Specify: A Bayesian Factorization of Vision Language Action Policy](https://mlsanity.com/paper/2512.11218/) (2025)
- [ETA: A New Agentic Paradigm for Embodied Tasks](https://mlsanity.com/paper/2608.03924/) (2026)
- [A Few Words Go a Long Way: Language Guided Robot Policy Synthesis](https://mlsanity.com/paper/2607.23784/) (2026)
- [AnySlot: Goal-Conditioned Vision-Language-Action Policies for Zero-Shot Slot-Level Placement](https://mlsanity.com/paper/2604.10432/) (2026)
- [GOPLA: Generalizable Object Placement Learning via Synthetic Augmentation of Human Arrangement](https://mlsanity.com/paper/2510.14627/) (2025)
- [GeoAlign: Beyond Semantics with State-Guided Spatial Alignment in VLA Models](https://mlsanity.com/paper/2606.03240/) (2026)
- [GIF: Agentic Generation of Interactive and Functional Object Compositions for Robot Learning](https://mlsanity.com/paper/2609.05927/) (2026)
- [Placeit! A Framework for Learning Robot Object Placement Skills](https://mlsanity.com/paper/2510.09267/) (2025)
- [LAMP: Lift Image-Editing as General 3D Priors for Open-world Manipulation](https://mlsanity.com/paper/2604.08475/) (2026)
- [Hybrid Residual Reinforcement Learning for Contact-Rich Robotic Book Insertion](https://mlsanity.com/paper/2609.19962/) (2026)

## Abstract (arXiv)

Object placement in robotic tasks is inherently challenging due to the diversity of object geometries and placement configurations. To address this, we propose AnyPlace, a two-stage method trained entirely on synthetic data, capable of predicting a wide range of feasible placement poses for real-world tasks. Our key insight is that by leveraging a Vision-Language Model (VLM) to identify rough placement locations, we focus only on the relevant regions for local placement, which enables us to train the low-level placement-pose-prediction model to capture diverse placements efficiently. For training, we generate a fully synthetic dataset of randomly generated objects in different placement configurations (insertion, stacking, hanging) and train local placement-prediction models. We conduct extensive evaluations in simulation, demonstrating that our method outperforms baselines in terms of success rate, coverage of possible placement modes, and precision. In real-world experiments, we show how our approach directly transfers models trained purely on synthetic data to the real world, where it successfully performs placements in scenarios where other models struggle -- such as with varying object geometries, diverse placement modes, and achieving high precision for fine placement. More at: https://any-place.github.io.