PackerBot: Variable-Sized Product Packing with Heuristic Deep Reinforcement Learning

Product packing is a typical application in ware-house automation that aims to pick objects from unstructured piles and place them into bins with optimized placing policy. However, it still remains a significant challenge to finish the product packing tasks in general logistics scenarios where the objects are variable-sized and the configurations are complex. In this work, we present the PackerBot, a complete robotic pipeline for performing variable-sized product packing in unstructured scenes. First, by leveraging the imperfect experience of human packer, we propose a heuristic DRL framework for learning optimal online 3D bin packing policy. Then we integrate it with a 6-DoF suction-based picking module and a product size estimation module, leading to a complete product packing system, namely the PackerBot. Extensive experimental results show that our method achieves the state-of-the-art performance in both simulated and real-world tests. The video demonstration is available at: https://vsislab.github.io/packerbot.

Authors

• Zifei Yang
• Shuo Yang
• Shuai Song
• Wei Zhang 0021
• Ran Song 0001
• Jiyu Cheng
• Yibin Li 0001

Keywords

• Three-dimensional displays
• Automation
• Pipelines
• Buildings
• Estimation
• Reinforcement learning
• Encoding
• Deep Reinforcement Learning
• Product Packaging
• Types Of Applications
• Complete System
• Optimal Policy
• Video Presentation
• Complete Pipeline
• Real-world Test
• Deep Reinforcement Learning Framework
• Heuristic Framework
• Bin Packing
• Optimization Problem
• Human Experience
• Workspace
• Point Cloud
• Simulation Environment
• Robotic System
• Space Use
• Heuristic Algorithm
• Depth Images
• Automated Guided Vehicles
• Space Utilization
• Reward Function
• Robotic Arm
• Transfer Strategy
• Markov Decision Process
• Policy Learning
• Packing Problem
• Real-world Tasks
• Deep Reinforcement Learning Method