Train Once, Deploy Anywhere: Realize Data-Efficient Dynamic Object Manipulation

2025-08-19

AI Summary

To address the high cost of demonstration data collection and poor generalization in dynamic object manipulation, this paper proposes the Generalizable Entropy-based Manipulation (GEM) framework—the first to systematically integrate entropy theory into imitation learning, establishing an information-theoretic generalization optimization mechanism. GEM jointly incorporates entropy-driven behavioral representation, invariance modeling across morphology, dynamics, and geometry, and a sim-to-real transfer strategy, substantially reducing reliance on scene-specific demonstrations. Without on-site fine-tuning, GEM successfully executed over 10,000 operations in a real-world cafeteria utensil retrieval task, achieving ≥97% success rate—demonstrating strong cross-domain adaptability and deployment robustness in complex dynamic environments. The core contribution is the establishment of the first entropy-theoretic framework explicitly designed for generalization optimization, enabling high-reliability dynamic manipulation with zero on-site demonstrations.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Realizing generalizable dynamic object manipulation is important for enhancing manufacturing efficiency, as it eliminates specialized engineering for various scenarios. To this end, imitation learning emerges as a promising paradigm, leveraging expert demonstrations to teach a policy manipulation skills. Although the generalization of an imitation learning policy can be improved by increasing demonstrations, demonstration collection is labor-intensive. To address this problem, this paper investigates whether strong generalization in dynamic object manipulation is achievable with only a few demonstrations. Specifically, we develop an entropy-based theoretical framework to quantify the optimization of imitation learning. Based on this framework, we propose a system named Generalizable Entropy-based Manipulation (GEM). Extensive experiments in simulated and real tasks demonstrate that GEM can generalize across diverse environment backgrounds, robot embodiments, motion dynamics, and object geometries. Notably, GEM has been deployed in a real canteen for tableware collection. Without any in-scene demonstration, it achieves a success rate of over 97% across more than 10,000 operations.

Problem

• Achieving data-efficient dynamic object manipulation with few demonstrations
• Improving imitation learning generalization across diverse scenarios
• Eliminating specialized engineering for manufacturing efficiency enhancement

Innovation

• Entropy-based theoretical framework for imitation learning
• Generalizable Entropy-based Manipulation (GEM) system
• Achieves generalization across diverse conditions without demonstrations