Isaac Lab is a unified GPU-native platform combining high-fidelity physics, photorealistic rendering, multi-frequency sensors, domain randomization, and learning pipelines for scalable multi-modal robot policy training.
Parkour in the wild: Learning a general and extensible agile locomotion policy using multi-expert distillation and rl fine-tuning
3 Pith papers cite this work. Polarity classification is still indexing.
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cs.RO 3verdicts
UNVERDICTED 3representative citing papers
Adding an actuated sagittal spine to a simulated quadruped increases agility and allows it to clear higher obstacles, steeper slopes, and tighter passages than the rigid-spine baseline.
Sparsely gated MoE policies double the success rate of a real Unitree Go2 quadruped on large-obstacle parkour versus matched-active-parameter MLP baselines while cutting inference time compared with a scaled-up MLP.
citing papers explorer
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Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
Isaac Lab is a unified GPU-native platform combining high-fidelity physics, photorealistic rendering, multi-frequency sensors, domain randomization, and learning pipelines for scalable multi-modal robot policy training.
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Evaluation of an Actuated Spine in Agile Quadruped Locomotion
Adding an actuated sagittal spine to a simulated quadruped increases agility and allows it to clear higher obstacles, steeper slopes, and tighter passages than the rigid-spine baseline.
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Quadruped Parkour Learning: Sparsely Gated Mixture of Experts with Visual Input
Sparsely gated MoE policies double the success rate of a real Unitree Go2 quadruped on large-obstacle parkour versus matched-active-parameter MLP baselines while cutting inference time compared with a scaled-up MLP.