Energy-based regularization on residual dynamics learning improves neural MPC for aerial robots, cutting positional error 23% versus analytical models and boosting stability over unregularized neural MPC in real flights.
aca- dos—a modular open-source framework for fast embedded optimal control
3 Pith papers cite this work. Polarity classification is still indexing.
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verdicts
UNVERDICTED 3representative citing papers
COSMIK-MPPI delivers 100% task success and fixed 22 ms runtime for safe robot-human collision avoidance by terminating constraint-violating trajectories instead of penalizing them.
A multi-timescale MPC for slow-fast systems switches to reduced slow-dynamics models and uses exponentially increasing integration step sizes to deliver up to 10x speed-ups in robotic control simulations.
citing papers explorer
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Energy-based Regularization for Learning Residual Dynamics in Neural MPC for Omnidirectional Aerial Robots
Energy-based regularization on residual dynamics learning improves neural MPC for aerial robots, cutting positional error 23% versus analytical models and boosting stability over unregularized neural MPC in real flights.
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COSMIK-MPPI: Scaling Constrained Model Predictive Control to Collision Avoidance in Close-Proximity Dynamic Human Environments
COSMIK-MPPI delivers 100% task success and fixed 22 ms runtime for safe robot-human collision avoidance by terminating constraint-violating trajectories instead of penalizing them.
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Multi-Timescale Model Predictive Control for Slow-Fast Systems
A multi-timescale MPC for slow-fast systems switches to reduced slow-dynamics models and uses exponentially increasing integration step sizes to deliver up to 10x speed-ups in robotic control simulations.