AnyPlace: Learning Generalizable Object Placement for Robot Manipulation

Yuchi Zhao, Miroslav Bogdanovic, Chengyuan Luo, Steven Tohme, Kourosh Darvish, Alan Aspuru-Guzik, Florian Shkurti, Animesh Garg

Keywords: Pick and Place, Robot Manipulation, Synthetic Dataset

TL;DR: 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.

Abstract: Object placement in robotic tasks is inherently challenging due to the diversity of object geometries and placement configurations. We address this with 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 approximate placement locations, we can focus only on the relevant regions for precise local placement, which enables us to train the low-level placement-pose-prediction model to capture multimodal placements efficiently. For training, we generate a fully synthetic dataset comprising 13 categories of randomly generated objects in 5370 different placement poses across three configurations (insertion, stacking, hanging) and train local placement-prediction models. We extensively evaluate our method in high-fidelity simulation and show that it consistently outperforms baseline approaches across all three tasks in terms of success rate, coverage of placement modes, and precision. In real-world experiments, our method achieves an average success and coverage rate of 76% across three tasks, where most baseline methods fail completely. We further validate the generalization of our approach on 16 real-world placement tasks, demonstrating that models trained purely on synthetic data can be directly transferred to the real world in a zero-shot setting. More at: https://anyplace-pnp.github.io.

Submission Number: 425