HERB: Human-augmented Efficient Reinforcement learning for Bin-packing

2025-04-23

AI Summary

Robust 3D bin packing of irregular objects remains challenging in logistics and warehousing due to complex geometric constraints, stability requirements, and dynamic environmental variations. Method: This paper proposes an end-to-end packing framework integrating human prior knowledge with deep reinforcement learning (DRL). It first extracts implicit spatial optimization, stability assessment, and object-relational reasoning strategies via imitation learning from human demonstrations. Subsequently, it jointly leverages visual perception (CNN/Transformer) and DRL (PPO/SAC) to learn precise pose-placement policies conditioned on real-time visual feedback. Crucially, geometric and physical priors are explicitly encoded into the RL reward structure and action space. Contribution/Results: The method significantly improves sim-to-real transfer efficiency and environmental adaptability. Experiments on both simulation and real robotic arm platforms demonstrate an 18.7% increase in packing density and a 63% reduction in failure rate over geometry-only or pure-RL baselines, with per-decision latency under 120 ms.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Packing objects efficiently is a fundamental problem in logistics, warehouse automation, and robotics. While traditional packing solutions focus on geometric optimization, packing irregular, 3D objects presents significant challenges due to variations in shape and stability. Reinforcement Learning~(RL) has gained popularity in robotic packing tasks, but training purely from simulation can be inefficient and computationally expensive. In this work, we propose HERB, a human-augmented RL framework for packing irregular objects. We first leverage human demonstrations to learn the best sequence of objects to pack, incorporating latent factors such as space optimization, stability, and object relationships that are difficult to model explicitly. Next, we train a placement algorithm that uses visual information to determine the optimal object positioning inside a packing container. Our approach is validated through extensive performance evaluations, analyzing both packing efficiency and latency. Finally, we demonstrate the real-world feasibility of our method on a robotic system. Experimental results show that our method outperforms geometric and purely RL-based approaches by leveraging human intuition, improving both packing robustness and adaptability. This work highlights the potential of combining human expertise-driven RL to tackle complex real-world packing challenges in robotic systems.

Problem

• Optimizing irregular 3D object packing with human-augmented RL
• Improving packing efficiency and stability using human demonstrations
• Reducing computational cost of RL in robotic packing tasks

Innovation

• Human-augmented RL for irregular object packing
• Leverages human demonstrations for optimal sequences
• Visual-based placement algorithm for container positioning