基于事件的四足机器人敏捷物体抓取
Event-based Agile Object Catching with a Quadrupedal Robot
AI总结：
针对四足机器人在动态环境中因传感器带宽-延迟权衡导致反应受限的问题，本研究通过配备事件相机实现低延迟感知，首次展示了四足机器人高速抓取物体的能力，在4米距离抓取15 m/s物体的成功率达83%。
摘要：
四足机器人正逐渐征服各种室内外应用场景，这得益于它们在具有挑战性的不平坦地形中导航的能力。外部感知信息极大地增强了这一能力，因为感知周围环境使它们能够调整控制器，从而实现更高的鲁棒性。然而，诸如LiDAR和RGB相机之类的传感器在高度动态的环境中无法提供足够的信息以实现快速精确的反应，因为它们存在带宽-延迟权衡问题。在高帧率下它们需要大量带宽，而在低帧率下则具有显著的感知延迟，从而限制了它们在资源受限平台上的多功能性。在这项工作中，我们通过为四足机器人配备事件相机来解决这一问题，由于其异步和稀疏的操作特性，事件相机不受这种权衡的影响。利用事件的低延迟特性，我们突破了四足机器人敏捷性的极限，并首次展示了高速球类抓取能力。我们展示了配备事件相机的四足机器人能够从4米距离抓取速度高达15 m/s的物体，成功率达到83%。使用VGA事件相机，我们的方法在NVIDIA Jetson Orin上以100 Hz的频率运行。
英文摘要：
Quadrupedal robots are conquering various indoor and outdoor applications due to their ability to navigate challenging uneven terrains. Exteroceptive information greatly enhances this capability since perceiving their surroundings allows them to adapt their controller and thus achieve higher levels of robustness. However, sensors such as LiDARs and RGB cameras do not provide sufficient information to quickly and precisely react in a highly dynamic environment since they suffer from a bandwidth-latency tradeoff. They require significant bandwidth at high frame rates while featuring significant perceptual latency at lower frame rates, thereby limiting their versatility on resource-constrained platforms. In this work, we tackle this problem by equipping our quadruped with an event camera, which does not suffer from this tradeoff due to its asynchronous and sparse operation. In leveraging the low latency of the events, we push the limits of quadruped agility and demonstrate high-speed ball catching for the first time. We show that our quadruped equipped with an event camera can catch objects with speeds up to 15 m/s from 4 meters, with a success rate of 83%. Using a VGA event camera, our method runs at 100 Hz on an NVIDIA Jetson Orin.
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。
