Deployment of robotic systems in the real world requires a certain level of robustness in order to deal with uncertainty factors, such as mismatches in the dynamics model, noise in sensor readings, and communication delays. Some approaches tackle these issues reactively at the control stage. However, regardless of the controller, online motion execution can only be as robust as the system capabilities allow at any given state. This is why it is important to have good motion plans to begin with, where robustness is considered proactively. To this end, we propose a metric (derived from first principles) for representing robustness against external disturbances. We then use this metric within our trajectory optimization framework for solving complex loco-manipulation tasks. Through our experiments, we show that trajectories generated using our approach can resist a greater range of forces originating from any possible direction. By using our method, we can compute trajectories that solve tasks as effectively as before, with the added benefit of being able to counteract stronger disturbances in worst-case scenarios.

翻译：摘要：在现实世界中部署机器人系统需要具备一定程度的鲁棒性，以应对动力学模型失配、传感器读数噪声、通信延迟等不确定性因素。部分方法在控制阶段通过反应式方式处理上述问题。然而，无论采用何种控制器，在线运动执行的鲁棒性始终受限于系统在任意给定状态下的能力边界。正因如此，预先制定考虑鲁棒性的优质运动规划至关重要。为此，我们提出了一种基于第一性原理的度量指标，用于表征系统对外部扰动的鲁棒性。随后将该指标融入轨迹优化框架中，以解决复杂的移动操作任务。实验表明，采用本方法生成的轨迹能够抵抗来自任意方向更广范围的外力作用。通过本方法计算的轨迹不仅能以同等效率完成既定任务，还能在最坏情况下有效抑制更强烈的扰动。