Achieving reactive robot behavior in complex dynamic environments is still challenging as it relies on being able to solve trajectory optimization problems quickly enough, such that we can replan the future motion at frequencies which are sufficiently high for the task at hand. We argue that current limitations in Model Predictive Control (MPC) for robot manipulators arise from inefficient, high-dimensional trajectory representations and the negligence of time-optimality in the trajectory optimization process. Therefore, we propose a motion optimization framework that optimizes jointly over space and time, generating smooth and timing-optimal robot trajectories in joint-space. While being task-agnostic, our formulation can incorporate additional task-specific requirements, such as collision avoidance, and yet maintain real-time control rates, demonstrated in simulation and real-world robot experiments on closed-loop manipulation. For additional material, please visit https://sites.google.com/oxfordrobotics.institute/vp-sto.

翻译：在复杂动态环境中实现机器人的反应性行为仍具挑战性，因为这依赖于能够快速求解轨迹优化问题，以便以任务所需足够高的频率重新规划未来运动。我们认为，当前机器人机械臂模型预测控制（MPC）的局限性源于低效的高维轨迹表示，以及轨迹优化过程中对时间最优性的忽视。因此，我们提出了一种联合优化空间与时间的运动优化框架，可在关节空间中生成平滑且时间最优的机器人轨迹。该框架与任务无关，同时可融入避碰等特定任务需求，并保持实时控制频率。通过闭环操作仿真与真实机器人实验验证了其有效性。更多资料请访问 https://sites.google.com/oxfordrobotics.institute/vp-sto。