Numerical optimization and dynamic models have given roboticists the tools to implement instantaneous whole-body control (e.g. finding joint torques at time t to track at best a reference trajectory). However, it provided no turnkey solution as to whole-body planning, where successful planners were historically grounded on stochastic sampling and state-space discretization rather than active sets and gradient descents. Yet, this does not mean that the two fields are hermetically separated. In this talk, we will go through the recent history of Time-Optimal Path Parameterization (TOPP), an optimization routine that has been successfully applied to both motion planning and predictive control. A key feature of TOPP is its ability to return not only optimal solutions for a given dynamic model, but also higher-level information such as intervals of reachable velocities. We will discuss these practicalities and show both simulation and real-hardware applications.
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