AnyPlace: Learning Generalized Object Placement for Robot Manipulation

2025-02-06

AI Summary

Predicting generalizable 6-DoF placement poses for diverse geometric objects under complex configurations (e.g., insertion, stacking, hanging) remains challenging in robotic manipulation. Method: We propose the first generalized placement model trained exclusively on synthetic data, using a two-stage cascaded architecture: (1) a vision-language model (VLM) to localize coarse feasible regions, followed by (2) a local pose regression network to predict high-accuracy, physically valid 6-DoF poses. Contribution/Results: Our key innovation is a VLM-guided region-focusing mechanism that efficiently models dense, multimodal, shape-agnostic feasible pose distributions. In simulation, the method achieves state-of-the-art performance—supporting broader placement modalities and yielding higher pose accuracy. Crucially, without any fine-tuning, it successfully deploys on real robots to perform delicate tasks such as precision insertion and multi-object stacking, demonstrating significantly improved cross-geometry and cross-task generalization.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• Addresses object placement in robotic tasks
• Predicts feasible poses using synthetic data
• Transfers synthetic training to real-world scenarios

Innovation

• Two-stage method on synthetic data
• Vision-Language Model identifies regions
• Local placement-pose-prediction model trained