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.
Learning quadruped locomotion using differentiable simulation
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SDPG is a new on-policy visual RL algorithm that estimates gradients via stochastic perturbations of rollouts, achieving faster training and lower memory use than baselines on visual MuJoCo tasks while adding new robotics benchmarks and sim-to-real results.
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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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Efficient On-policy Visual-RL via Stochastic Decoupled Policy Gradient
SDPG is a new on-policy visual RL algorithm that estimates gradients via stochastic perturbations of rollouts, achieving faster training and lower memory use than baselines on visual MuJoCo tasks while adding new robotics benchmarks and sim-to-real results.