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.
@article{arxiv.2502.04531,
title = {AnyPlace: Learning Generalized Object Placement for Robot Manipulation},
author = {Yuchi Zhao and Miroslav Bogdanovic and Chengyuan Luo and Steven Tohme and Kourosh Darvish and Alán Aspuru-Guzik and Florian Shkurti and Animesh Garg},
journal= {arXiv preprint arXiv:2502.04531},
year = {2025}
}