Core Insights - Boston Dynamics' Spot robot can lift a 15 kg tire in just 3.7 seconds, showcasing advanced dynamic whole-body manipulation techniques [1][11] - The robot's performance exceeds traditional static assumptions, demonstrating the ability to coordinate movements effectively beyond its maximum lifting capacity [13] Group 1: Dynamic Whole-Body Manipulation - The method combines sampling and learning to enable the robot to perform tasks requiring coordination of arms, legs, and torso [1][2] - A hierarchical control approach divides the control problem into two layers: low-level control for balance and stability, and high-level control for task-specific strategies [2][14] Group 2: Control Strategies - The low-level control uses reinforcement learning to manage motor torque for stability, while high-level control employs sampling-based strategies for tasks like tire alignment and stacking [2][7] - The sampling controller simulates multiple future scenarios in parallel to identify the most effective actions for task completion [3][5] Group 3: Performance Metrics - The robot achieved an average time of 5.9 seconds per tire, nearly matching human operational speed [11] - The dynamic coordination allows the robot to handle weights significantly exceeding its peak lifting capabilities, expanding its operational range [13][14] Group 4: Learning and Adaptation - The training process incorporates randomization of object properties to bridge the gap between simulation and real-world application [10] - The use of an asymmetric actor-critic architecture for training enhances the robot's ability to adapt to complex dynamics and contact mechanics [8][10]

搬运的轮胎重达 15 公斤，相当于Spot自身重量的一半（32.7千克），并远超其最大臂力。 而且，搬起轮胎后，它还能将轮胎滚动到指定位置。 henry 发自 凹非寺 量子位 | 公众号 QbitAI 机器狗搬轮胎，"五只腿"齐发力！ 在 波士顿动力人工智能研究所 的最新方法—— 结合采样与学习的动态全身操作 中，波士顿动力的机器狗 Spot 最快仅用 3.7 秒就能搬起轮 胎。 甚至还能把一个轮胎叠到另一个轮胎上面（还会用头帮忙顶一下）。 这一方法克服了传统操作策略（如摇操）在不同机器人形态学结构上的迁移限制，并通过 分层控制 实现了机器狗四肢与全身的协调动力学操 作。 网友表示，狗子快去回收站去搬轮胎吧！ 这是怎么做到的？ 结合采样与学习的动态全身操作 总的来说，结合采样与学习的动态全身操作 （Combining Sampling and Learning for Dynamic Whole-Body Manipulation）利用强化学 习与基于采样的控制（sampling-based control）相结合的方法，使机器人能够执行需要手臂、双腿和躯干协同配合的动态力交互任务。 为应对复杂的操作任务，研 ...