To dynamically traverse challenging terrain, legged robots need to continually perceive and reason about upcoming features, adjust the locations and timings of future footfalls and leverage momentum strategically. We present a pipeline that enables flexibly-parametrized trajectories for perceptive and dynamic quadruped locomotion to be optimized in an online, receding-horizon manner. The initial guess passed to the optimizer affects the computation needed to achieve convergence and the quality of the solution. We consider two methods for generating good guesses. The first is a heuristic initializer which provides a simple guess and requires significant optimization but is nonetheless suitable for adaptation to upcoming terrain. We demonstrate experiments using the ANYmal C quadruped, with fully onboard sensing and computation, to cross obstacles at moderate speeds using this technique. Our second approach uses latent-mode trajectory regression (LMTR) to imitate expert data - while avoiding invalid interpolations between distinct behaviors - such that minimal optimization is needed. This enables high-speed motions that make more expansive use of the robot's capabilities. We demonstrate it on flat ground with the real robot and provide numerical trials that progress toward deployment on terrain. These results illustrate a paradigm for advancing beyond short-horizon dynamic reactions, toward the type of intuitive and adaptive locomotion planning exhibited by animals and humans.

翻译：对于充满活力、充满挑战的地形,脚步机器人需要不断认识和理解即将到来的地貌,调整未来脚步的位置和时间,并战略性地利用动力。我们展示了一条管道,能够使感知和动态四重振动的移动速度以在线方式优化,并逐渐退缩。最初的猜测传递到优化会影响实现趋同和解决方案质量所需的计算方法。我们考虑两种方法来产生良好的猜测。第一个是超速初始化器,它提供了简单的猜测,需要大大优化,但仍适合适应即将到来的地形。我们展示了使用Anymal C四重曲的实验,完全在机上进行感测和计算,以便以中速超速跨越障碍。我们的第二种方法使用潜温极轨回归(LMTR)来模仿专家数据,同时避免不同行为之间出现无效的内断层。我们需要最起码的优化。这使得高速移动动作能够使机器人的能力得到更大的利用,但仍然适合适应即将到的地形。我们展示了使用Any C四重轨的实验,我们用平坦式的地面上展示了真实的机器人的适应性模型,并展示了向真实的地面的动态的动作。