FALCON incorporates psychologically grounded fatigue curves into learning-to-defer via a CMDP formulation and PPO-Lagrangian optimization, outperforming prior L2D methods and generalizing to unseen fatigue patterns on the new FA-L2D benchmark.
Gradients are not all you need
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Differentiable relaxation of LTL automata via soft labeling enables gradient-based RL from formal specifications, with theoretical bounds on discrete-differentiable discrepancy and up to 2x returns on nonlinear tasks.
An end-to-end vision-based framework enables UAVs to traverse complex irregular gaps in unseen environments by mapping depth images to SE(3) control commands using differentiable simulation.
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Fatigue-Aware Learning to Defer via Constrained Optimisation
FALCON incorporates psychologically grounded fatigue curves into learning-to-defer via a CMDP formulation and PPO-Lagrangian optimization, outperforming prior L2D methods and generalizing to unseen fatigue patterns on the new FA-L2D benchmark.
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Accelerated Learning with Linear Temporal Logic using Differentiable Simulation
Differentiable relaxation of LTL automata via soft labeling enables gradient-based RL from formal specifications, with theoretical bounds on discrete-differentiable discrepancy and up to 2x returns on nonlinear tasks.
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Vision-Based End-to-End Learning for UAV Traversal of Irregular Gaps via Differentiable Simulation
An end-to-end vision-based framework enables UAVs to traverse complex irregular gaps in unseen environments by mapping depth images to SE(3) control commands using differentiable simulation.
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