Core Insights - The article discusses the challenges faced by robots in dexterous manipulation, highlighting issues such as high data requirements, difficulties in virtual-to-real transfer, and weak generalization capabilities [2] - A new hierarchical RL-MPC framework inspired by human operation logic is proposed, achieving nearly 100% task success rate, 10 times data efficiency improvement, and zero-shot virtual-to-real transfer [2][4] Challenges in Traditional Dexterous Manipulation - Traditional approaches struggle to balance learning efficiency, robustness, and generalization, with three main issues identified: 1. End-to-end vision methods require massive data for learning non-smooth contact dynamics, leading to low efficiency in long-term tasks [3] 2. Motion strategies face significant gaps in performance across different object geometries and scenes [3] 3. Traditional model control lacks flexibility and adaptability in open environments with diverse object shapes [3] Innovations in the Hierarchical RL-MPC Framework - The framework's core innovation is the "Contact Intention," which serves as an interface connecting high-level decision-making and low-level execution, structured into three layers and two modules [4][6] - High-level RL focuses on predicting contact intentions based on scene observations, while low-level MPC specializes in executing contact dynamics [4][12] High-Level RL Strategy - The high-level RL strategy employs a three-component observation space that includes geometry, target, and collision information, enhancing the strategy's environmental awareness [7] - The framework uses indirect prediction of MPC weights to define sub-goals, improving learning efficiency by allowing flexible switching between sub-goals [8] - A dual-branch network architecture balances local details and global context, optimizing feature extraction for both [9] Low-Level MPC Execution - The low-level MPC utilizes a complementary free model predictive control (ComFree-MPC) to ensure stability and adaptability in contact actions, operating at a high frequency of 100Hz [12][16] - The optimization objectives are designed to strictly adhere to high-level intentions, ensuring quick responses to disturbances [17] Experimental Validation - The framework demonstrated strong performance in two non-prehensile tasks, achieving a success rate of 97.34% for unseen objects in a pushing task and 100% in 3D redirection tasks [20][24] - The data efficiency of the framework significantly outperformed end-to-end strategies, requiring only 15,000 RL decision steps to achieve 100% success rate compared to 600,000 steps for traditional methods [26] Robustness and Virtual-to-Real Transfer - The framework exhibited high robustness against various disturbances, maintaining performance while traditional methods failed under similar conditions [25][29] - The strategy was successfully deployed on real robots without any fine-tuning, achieving high success rates across various objects [30] Limitations and Future Directions - The framework currently relies on accurate pose estimation, which can lead to failures in real-world scenarios, indicating a need for integrated perception-planning-control designs [36] - There are challenges in scalability with multiple end-effectors, suggesting future work should focus on optimizing contact intention representation [36] Conclusion - The hierarchical RL-MPC framework represents a significant advancement in dexterous manipulation, effectively combining decision-making flexibility with execution stability, paving the way for broader applications in robotics [37]

点击下方 卡片 ，关注" 具身智能 之心 "公众号 先来看一段视频。 ★ 该项成果的一作为阿米奥联合创始人兼技术负责人冯骞，硕博均就读于德国慕尼黑工业大学，师从机 器人泰斗Alois Knoll，曾是思灵机器人早期员工、研究科学家。本次IROS2025，冯博将会在 Deep Learning in Grasping and Manipulation论坛上针对这项工作发表演讲。 机器人灵巧操作领域研究进展 领域痛点 机器人灵巧操作（如多手指抓取）是实现 "类人机器人" 的关键，但现有方案存在三大核心问题： 当机器人面对陌生物体， 如何靠少量演示、单视角观测就精准抓取？ LensDFF ，这项由阿米奥机器人给出 了颠覆性方案—— 它跳出传统 "依赖多视角数据、额外训练对齐网络" 的思路，直接用语言特征作为 "语义 锚点"，将 CLIP 提取的 2D 视觉特征，通过动态投影公式对齐到 3D 空间，从根源解决跨视角特征不一致 问题，且全程无需微调。 更关键的是，它把 5 种抓取原语（捏 / 钩 / 三脚架等）融入少样本演示，搭配 "法向量引导初始化 + 低维 eigengrasp 优化"，让 DLR-HIT 灵巧手能 ...