从纯小白到具身算法工程师的打怪之路

Core Insights - The article discusses the evolution and research directions in Visual Language Action (VLA), Visual Language Navigation (VLN), and reinforcement learning in robotics, highlighting the importance of these technologies in enhancing robot capabilities and performance [1][2][5][9]. VLA Direction - VLA systems consist of visual perception processing, language instruction understanding, and action strategy networks, categorized into three paradigms: explicit end-to-end VLA, implicit end-to-end VLA, and hierarchical end-to-end VLA [1][2]. - Explicit end-to-end VLA compresses visual and language information into a joint representation, which is then mapped to action space, leveraging various architectures and models to achieve good performance [1]. - Implicit end-to-end VLA focuses on interpretability by predicting future states using video diffusion models, enhancing the potential for scaling VLA models [2]. - Hierarchical end-to-end VLA aims to utilize the characteristics of large models to improve generalization while maintaining efficiency for downstream execution [2]. VLN Direction - VLN systems are composed of visual language encoders, environmental history representation, and action strategies, requiring effective information compression from visual and language inputs [5][6]. - The choice of encoder and whether to project visual and language representations into a common space are critical issues, with current trends favoring pre-trained models on large datasets and the use of large language models (LLM) for instruction decomposition [6]. - VLN robots operate in a sequential decision-making task, accumulating historical information to inform future actions, with implicit methods representing past information as latent variables [6]. - Object Navigation within VLN emphasizes identifying target objects based on category information, reducing the need for detailed instructions and enhancing exploration capabilities [7]. Reinforcement Learning & Legged Robots - Reinforcement learning is crucial for legged robots, covering various aspects such as kinematics, dynamics, multi-modal sensor fusion, and advanced algorithms for task adaptation [9][10]. - Key areas include gait planning, balance control for bipedal robots, and the application of deep reinforcement learning and imitation learning for multi-task training [10]. - Techniques like domain randomization and safety mechanisms are essential for ensuring successful real-world deployment of robotic systems [10]. Diffusion Policy - The introduction of diffusion models in robotics has led to significant advancements, with the Diffusion Policy achieving an average performance improvement of 46.9% in various simulation environments [21][22]. - The Robotic Diffusion Transformer (RDT), with 1.2 billion parameters, showcases strong zero-shot generalization capabilities and the ability to learn new skills with minimal examples [22]. - The application of diffusion strategies is expanding beyond robotic manipulation to areas like autonomous navigation and dexterous grasping, enhancing task success rates through real-time environmental adaptation [22][23]. - Recent developments in diffusion strategies include advancements in 3D applications and the integration of safety and online reinforcement learning, opening new research avenues [23].