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Core Viewpoint - The article emphasizes the significant advancements in the field of robotics, particularly in robot learning, driven by the development of artificial intelligence technologies such as large models and multi-modal models. This shift has transformed traditional robotics into a learning-based paradigm, opening new potentials for autonomous decision-making robots [2]. Group 1: Introduction to Robot Learning - The article highlights the evolution of robotics from explicit modeling to implicit modeling, marking a fundamental change in motion generation methods. Traditional robotics relied on explicit modeling, while learning-based methods utilize deep reinforcement learning and expert demonstration learning for implicit modeling [15]. - A comprehensive tutorial provided by HuggingFace and researchers from Oxford University serves as a valuable resource for newcomers to modern robot learning, covering foundational principles of reinforcement learning and imitation learning [3][4]. Group 2: Learning-Based Robotics - Learning-based robotics simplifies the process from perception to action by training a unified high-level controller that can directly handle high-dimensional, unstructured perception-motion information without relying on a dynamics model [33]. - The tutorial addresses challenges in real-world applications, such as safety and efficiency issues during initial training phases, and high trial-and-error costs in physical environments. It introduces advanced techniques like simulator training and domain randomization to mitigate these risks [34][35]. Group 3: Reinforcement Learning - Reinforcement learning allows robots to autonomously learn optimal behavior strategies through trial and error, showcasing significant potential across various scenarios [28]. - The tutorial discusses the "Offline-to-Online" reinforcement learning framework, which enhances sample efficiency and safety by utilizing pre-collected expert data. The HIL-SERL method exemplifies this approach, enabling robots to master complex real-world tasks with near 100% success rates in just 1-2 hours of training [36][39]. Group 4: Imitation Learning - Imitation learning offers a more direct learning path for robots by replicating expert actions through behavior cloning, avoiding complex reward function designs and ensuring training safety [41]. - The tutorial presents advanced imitation learning methods based on generative models, such as Action Chunking with Transformers (ACT) and Diffusion Policy, which effectively model multi-modal data by learning the latent distribution of expert behaviors [42][43]. Group 5: Universal Robot Policies - The article envisions the future of robotics in developing universal robot policies capable of operating across tasks and devices, inspired by the emergence of large-scale open robot datasets and powerful visual-language models (VLMs) [52]. - Two cutting-edge VLA models, π₀ and SmolVLA, are highlighted for their ability to understand visual and language instructions and generate precise robot control commands, with SmolVLA being a compact, open-source model that significantly reduces application barriers [53][56].