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DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control 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 $\mathcal {L}_1$ adaptive control in closed-loop, without any fine-tuning
DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control DATT builds on a novel feedforward-feedback-adaptive control structure trained in simulation us- ing reinforcement learning When deployed on real hardware, DATT is augmented with a disturbance estimator using L 1adaptive control in closed-loop, without any fine-tuning
FARZI DATA: AUTOREGRESSIVE DATA DISTILLATION ABSTRACT We study data distillation for auto-regressive machine learning tasks, where the input and output have a strict left-to-right causal structure More specifically, we propose FARZI, which summarizes an event sequence dataset into a small number of synthetic sequences — FARZI DATA — which are optimized to maintain (if not improve) model performance compared to training on the full
RETHINKING ATTENTION WITH PERFORMERS - OpenReview ABSTRACT We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity, without relying on any priors such as sparsity or low-rankness To approximate softmax attention-kernels, Performers use a novel Fast Attention Via positive Orthogonal
Guanya Shi - OpenReview DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control Kevin Huang, Rwik Rana, Alexander Spitzer, Guanya Shi, Byron Boots Published: 30 Aug 2023, Last Modified: 19 Apr 2025 CoRL 2023 Oral View all 24 publications