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DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control
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DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control
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Precise arbitrary trajectory tracking for quadrotors is challenging due to unknown nonlinear dynamics, trajectory infeasibility, and actuation limits. To tackle these challenges, we present Deep Adaptive Trajectory Tracking (DATT), a learning-based approach that can precisely track arbitrary, potentially infeasible trajectories in the presence of large disturbances in the real world. DATT builds on a novel feedforward-feedback-adaptive control structure trained in simulation using reinforcement learning. When deployed on real hardware, DATT is augmented with a disturbance estimator using L1 adaptive control in closed-loop, without any fine-tuning. DATT significantly outperforms competitive adaptive nonlinear and model predictive controllers for both feasible smooth and infeasible trajectories in unsteady wind fields, including challenging scenarios where baselines completely fail. Moreover, DATT can efficiently run online with an inference time less than 3.2 ms, less than 1/4 of the adaptive nonlinear model predictive control baseline
Forward citations
Cited by 4 Pith papers
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MorphQuad: Morphable Quadrotor for Superhuman Maneuverability, Manipulation, and Resiliency
A dual-axis-gimbal quadrotor with almost-globally stable geometric control and energy-optimal thrust allocation achieves omnidirectional free and contact flight with fully onboard autonomy.
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Adapting Generalist Vehicle Models for High-Speed MPC Across Terrains
History-conditioned fine-tuning with targeted synthetic rollouts from a per-terrain bicycle model roughly halves 6 m/s trajectory tracking error against a fine-tuned AnyCar baseline.
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MorphQuad: Morphable Quadrotor for Superhuman Maneuverability, Manipulation, and Resiliency
A quadrotor with independently gimbaled, counter-rotating rotor pairs demonstrates continuous multi-revolution flight, human-wrist-level manipulation, and multidirectional wind rejection using fully onboard autonomy.
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Efficient Domain-Adaptive Policy Learning via Kernel Representation with Application to Quadrotor Control under Non-Stationary Disturbances
A quadrotor policy trained in 50 seconds of differentiable simulation adapts online by updating both random-Fourier-feature coefficients and bandwidth, improving tracking over DATT/MPC baselines in simulation and on C...
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