RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulated Environments

2025-09-21

AI Summary

To address the challenges of unifying modeling, evaluation, and reproducibility for imitation learning in robotic manipulation across simulation and real-world environments, this paper introduces the first integrated, general-purpose, scalable, and reproducible open framework. The framework uniformly supports cross-environment (Sim2Real) data collection, multimodal policy training—integrating vision, language, and action modalities—and standardized evaluation, via a modular pipeline compatible with diverse robot hardware and task configurations. Its core contributions are: (1) a cross-platform benchmarking suite covering multi-task, multi-robot, and multimodal policies; (2) seamless co-processing and evaluation alignment between simulated and real-world data; and (3) substantially improved experimental reproducibility and fairness in algorithmic comparison. Extensive validation on multiple physical robot platforms demonstrates its efficacy in efficient policy learning and Sim2Real transfer.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Computer Vision: Multi-modal Vision

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

RoboManipBaselines is an open framework for robot imitation learning that unifies data collection, training, and evaluation across simulation and real robots. We introduce it as a platform enabling systematic benchmarking of diverse tasks, robots, and multimodal policies with emphasis on integration, generality, extensibility, and reproducibility.

Problem

• Unifying data collection and training for robotic imitation learning
• Enabling systematic benchmarking across simulation and real robots
• Developing multimodal policies with emphasis on reproducibility

Innovation

• Unified framework for imitation learning
• Integration across simulation and real robots
• Systematic benchmarking of multimodal policies