波士顿动力狗gogo回来了！“五条腿”协同发力

Core Insights - The article discusses the advancements in Boston Dynamics' Spot robot, which can lift and manipulate a tire weighing 15 kg in just 3.7 seconds, showcasing its dynamic whole-body manipulation capabilities [3][31]. Group 1: Dynamic Whole-Body Manipulation - The method combines sampling and learning for dynamic whole-body manipulation, utilizing reinforcement learning and sampling-based control to enable coordinated tasks involving arms, legs, and torso [11][12]. - A hierarchical control approach is employed, dividing control problems into two complementary layers: a low layer for direct motor torque control and a high layer for task-specific strategies [12][13]. Group 2: Task Execution and Control Strategies - For tasks like tire alignment and stacking, the system uses sampling-based control to simulate potential future scenarios and discover optimal strategies [14]. - Reinforcement learning is applied to maintain stability during rolling tasks, capturing the necessary dynamic features and reactive control mechanisms [15][26]. Group 3: Performance and Efficiency - The Spot robot's performance in tire manipulation exceeds traditional static assumptions, demonstrating the ability to handle weights beyond its peak lifting capacity of 11 kg [35]. - The robot's dynamic coordination of movements allows it to efficiently perform tasks that were previously limited to slower, static methods [36][33]. Group 4: Simplification of Control Problems - Separating high-level and low-level control significantly simplifies the control challenges, allowing the high-level controller to focus on task completion without needing to reason about joint torques or stability constraints [37][38]. - The learned motion abstractions enable the high-level controller to operate in a simplified action space, enhancing computational feasibility and task execution efficiency [38].