Placeit! A Framework for Learning Robot Object Placement Skills

2025-10-10

AI Summary

High-quality pose data for robotic object placement is scarce, hindering learning-based approaches. Method: This paper proposes a quality-diversity (QD) optimization framework—first systematically applying QD algorithms to rigid-object placement pose generation. It autonomously discovers high-success-rate, geometrically diverse valid poses in simulation, covering planar placement, stacking, and insertion under multiple geometric constraints, without human annotation or task-specific rules. Contribution/Results: The method significantly improves pose coverage and validity over prior approaches. Integrated into an end-to-end grasp-and-place pipeline, it achieves 90% success across 120 real-world deployments. Moreover, it efficiently generates large-scale, diverse pose datasets, providing robust training data for robotic foundation models.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Robotics research has made significant strides in learning, yet mastering basic skills like object placement remains a fundamental challenge. A key bottleneck is the acquisition of large-scale, high-quality data, which is often a manual and laborious process. Inspired by Graspit!, a foundational work that used simulation to automatically generate dexterous grasp poses, we introduce Placeit!, an evolutionary-computation framework for generating valid placement positions for rigid objects. Placeit! is highly versatile, supporting tasks from placing objects on tables to stacking and inserting them. Our experiments show that by leveraging quality-diversity optimization, Placeit! significantly outperforms state-of-the-art methods across all scenarios for generating diverse valid poses. A pick&place pipeline built on our framework achieved a 90% success rate over 120 real-world deployments. This work positions Placeit! as a powerful tool for open-environment pick-and-place tasks and as a valuable engine for generating the data needed to train simulation-based foundation models in robotics.

Problem

• Automating robot object placement skills for rigid objects
• Overcoming data scarcity in robotic skill learning
• Generating diverse valid placement poses via simulation

Innovation

• Evolutionary computation framework for object placement
• Quality-diversity optimization generates diverse valid poses
• Simulation-based approach creates robotic training data