让机器人「不仅会想，还能准确去做」，VLA-R1把「推理+行动」带进真实世界

Core Insights - The article discusses the VLA-R1 model, which enhances reasoning in Vision-Language-Action (VLA) models by integrating chain-of-thought (CoT) supervision with reinforcement learning (RL) to improve both reasoning quality and execution accuracy [4][5]. Group 1: VLA-R1 Overview - VLA-R1 is a foundational model that emphasizes "reasoning first, then executing" [4]. - It combines CoT supervision with verifiable rewards from RL to optimize the reasoning and execution processes [4][5]. Group 2: Key Innovations - Two-stage training approach: The model first undergoes supervised fine-tuning (SFT) with explicit CoT supervision, followed by reinforcement learning based on GRPO to stabilize the transition from reasoning to action [6][8]. - Three types of verifiable rewards (RLVR) are introduced to ensure accurate perception, trajectory execution, and structured output [9][11]. - The VLA-CoT data engine generates a structured dataset of 13,000 visual-language-action samples to provide high-quality supervision signals for SFT [12][19]. Group 3: Experimental Results - VLA-R1 was evaluated across four levels: in-domain testing, out-of-domain testing, simulation platforms, and real robot experiments [16][17]. - In the in-domain benchmark, VLA-R1 achieved a perception IoU of 36.51, improving by 17.78% over the baseline [22]. - In real robot experiments, VLA-R1 demonstrated a success rate of 62.5% for affordance perception and 75% for trajectory execution under various environmental complexities [26]. Group 4: Applications - VLA-R1 is applicable in home automation tasks, such as object retrieval and organization in cluttered environments, by effectively reasoning through similar targets and multiple container options [34]. - It can also be utilized in warehouse picking and light industrial assembly processes, where it clarifies the relationships between parts, tools, and containers [34]. - The model's structured output format is suitable for educational demonstrations and automated assessments, allowing for easy evaluation of reasoning and execution steps [34].