A physics-aware residual framework decomposes Euler-Lagrange model mismatch into constrained inertia/Coriolis corrections plus a sparse history-dependent latent disturbance model adapted via Bayesian regression, improving prediction on multiple robot platforms.
Neurobem: Hybrid aerodynamic quadrotor model
5 Pith papers cite this work. Polarity classification is still indexing.
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Reinforcement learning sensorimotor policies enable quadrotors to traverse narrow gaps at extreme tilts with 5 cm clearance using only vision and proprioception, including reactive traversal of moving gaps.
LNN-Fly is a structured recurrent policy for continuous-time UAV obstacle avoidance trained with perturbed differentiable rollouts that shows improved tolerance to timing issues and zero-shot transfer to physical hardware with 100% success in real tests.
An RL-based outer-loop quadrotor controller augmented with an online Residual Dynamics Predictor for disturbance estimation and a data-efficient sim-to-real calibration bridge.
A nonlinear latent encoder plus linear latent decoder learns cross-coupled, history-dependent, and nonstationary residual dynamics for aerial manipulators, supporting online Bayesian adaptation and improved real-time MPC tracking.
citing papers explorer
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Physics-Aware Sparse Learning and Selective Online Adaptation for Euler-Lagrange Robot Dynamics
A physics-aware residual framework decomposes Euler-Lagrange model mismatch into constrained inertia/Coriolis corrections plus a sparse history-dependent latent disturbance model adapted via Bayesian regression, improving prediction on multiple robot platforms.
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Precise Aggressive Aerial Maneuvers with Sensorimotor Policies
Reinforcement learning sensorimotor policies enable quadrotors to traverse narrow gaps at extreme tilts with 5 cm clearance using only vision and proprioception, including reactive traversal of moving gaps.
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LNN-Fly: Continuous-Time UAV Navigation for Robust Obstacle Avoidance under Timing Mismatch
LNN-Fly is a structured recurrent policy for continuous-time UAV obstacle avoidance trained with perturbed differentiable rollouts that shows improved tolerance to timing issues and zero-shot transfer to physical hardware with 100% success in real tests.
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Adaptive Outer-Loop Control of Quadrotors via Reinforcement Learning
An RL-based outer-loop quadrotor controller augmented with an online Residual Dynamics Predictor for disturbance estimation and a data-efficient sim-to-real calibration bridge.
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Learning Cross-Coupled and Regime Dependent Dynamics for Aerial Manipulation
A nonlinear latent encoder plus linear latent decoder learns cross-coupled, history-dependent, and nonstationary residual dynamics for aerial manipulators, supporting online Bayesian adaptation and improved real-time MPC tracking.