A residual-learning planner with hard boundary-condition constraints generates near-minimum-energy robot trajectories in real time, at 87.3% of the optimal-control solver's performance near the training set and 50.8% far from it.
Learning the problem-optimum map: Analysis and ap- plication to global optimization in robotics,
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Real-Time Generation of Near-Minimum-Energy Trajectories via Constraint-Informed Residual Learning
A residual-learning planner with hard boundary-condition constraints generates near-minimum-energy robot trajectories in real time, at 87.3% of the optimal-control solver's performance near the training set and 50.8% far from it.