AnyPlace: Learning Generalized Object Placement for Robot Manipulation

• URL: http://arxiv.org/abs/2502.04531v1
• Date: Thu, 06 Feb 2025 22:04:13 GMT
• Title: AnyPlace: Learning Generalized Object Placement for Robot Manipulation
• Authors: Yuchi Zhao, Miroslav Bogdanovic, Chengyuan Luo, Steven Tohme, Kourosh Darvish, Alán Aspuru-Guzik, Florian Shkurti, Animesh Garg,
• Abstract summary: We propose AnyPlace, a two-stage method trained entirely on synthetic data. Our key insight is that by leveraging a Vision-Language Model, we focus only on the relevant regions for local placement. For training, we generate a fully synthetic dataset of randomly generated objects in different placement configurations. In real-world experiments, we show how our approach directly transfers models trained purely on synthetic data to the real world.
• Score: 37.725807003481904
• License:
• Abstract: 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.

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