使用黑盒控制器学习运动模型的四足机器人足部步态规划
Quadrupedal Footstep Planning using Learned Motion Models of a Black-Box Controller
AI总结：
本研究提出一种快速局部运动规划流水线，通过学习黑盒行走控制器的运动模型并结合A star算法变体，使四足机器人能够基于视觉信息安全自主地穿越崎岖地形。
摘要：
腿式机器人正日益进入新的领域和应用，包括搜救、巡检和物流。然而，要使此类系统在现实场景中具有价值，它们必须能够自主且鲁棒地在崎岖地形上导航。在许多情况下，市场上销售的机器人并不具备此类能力，仅能执行盲目运动。此外，其控制器无法由最终用户轻易修改，需要重新进行耗时较长的控制综合。在本研究中，我们提出了一种快速局部运动规划流水线，扩展了仅能跟踪高级参考速度的黑盒行走控制器的能力。更准确地说，我们为这种控制器学习了一组运动模型，将高级速度指令映射到质心（CoM）和足部步态运动。随后，我们将这些模型与 A star 算法的一种变体相结合，基于视觉信息规划质心轨迹、足部步态序列及相应的高级速度指令，使四足机器人能够按需安全地穿越崎岖地形。
英文摘要：
Legged robots are increasingly entering new domains and applications, including search and rescue, inspection, and logistics. However, for such systems to be valuable in real-world scenarios, they must be able to autonomously and robustly navigate irregular terrains. In many cases, robots that are sold on the market do not provide such abilities, being able to perform only blind locomotion. Furthermore, their controller cannot be easily modified by the end-user, requiring a new and time-consuming control synthesis. In this work, we present a fast local motion planning pipeline that extends the capabilities of a black-box walking controller that is only able to track high-level reference velocities. More precisely, we learn a set of motion models for such a controller that maps high-level velocity commands to Center of Mass (CoM) and footstep motions. We then integrate these models with a variant of the A star algorithm to plan the CoM trajectory, footstep sequences, and corresponding high-level velocity commands based on visual information, allowing the quadruped to safely traverse irregular terrains at demand.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
