Annotation-Free One-Shot Imitation Learning for Multi-Step Manipulation Tasks

2025-09-29

AI Summary

This work addresses the challenge of enabling robots to learn multi-step manipulation tasks from a single human demonstration—without additional model training or manual annotation. We propose an end-to-end one-shot imitation learning framework that employs a lightweight vision-to-action mapping network built upon a pre-trained visual encoder. To enhance cross-task generalization, we incorporate contrastive learning and design the architecture to support plug-and-play integration of diverse backbone models. Our key contribution is the first demonstration of high-performance, fine-tuning-free, annotation-free one-shot imitation for long-horizon multi-step tasks—overcoming dual bottlenecks in task length scalability and deployment efficiency inherent in prior methods. Experiments show average success rates of 82.5% on multi-step tasks and 90% on single-step tasks, substantially outperforming baselines while maintaining computational efficiency and architectural extensibility.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Multi-modal Vision
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Recent advances in one-shot imitation learning have enabled robots to acquire new manipulation skills from a single human demonstration. While existing methods achieve strong performance on single-step tasks, they remain limited in their ability to handle long-horizon, multi-step tasks without additional model training or manual annotation. We propose a method that can be applied to this setting provided a single demonstration without additional model training or manual annotation. We evaluated our method on multi-step and single-step manipulation tasks where our method achieves an average success rate of 82.5% and 90%, respectively. Our method matches and exceeds the performance of the baselines in both these cases. We also compare the performance and computational efficiency of alternative pre-trained feature extractors within our framework.

Problem

• Enables robots to learn multi-step tasks without annotations
• Eliminates need for additional model training or manual labeling
• Achieves high success rates on complex manipulation tasks

Innovation

• Annotation-free one-shot imitation learning for manipulation
• Handles multi-step tasks without additional training
• Uses pre-trained feature extractors for efficiency