AWARE is a hierarchical RL framework that enables wheeled-legged robots to perform high-dynamic reflexive obstacle evasion with emergent gaits in simulation and on the real M20 platform.
Learning robust perceptive locomotion for quadrupedal robots in the wild
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
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cs.RO 6years
2026 6representative citing papers
QuietWalk combines an inverse-dynamics-constrained PINN for GRF estimation with RL to produce low-impact humanoid locomotion policies that generalize across footwear, cutting mean noise by 7.17 dB on hardware.
LatentMimic decouples stylistic fidelity from geometric terrain constraints in quadruped locomotion via marginal latent divergence to a mocap prior and a dynamic replay buffer, yielding higher traversal success than motion-tracking baselines while preserving gait style.
AttenNKF augments InEKF with an attention-based neural compensator trained in latent space to correct foot-slip errors in legged robot state estimation.
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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Unleashing the Agility of Wheeled-Legged Robots for High-Dynamic Reflexive Obstacle Evasion
AWARE is a hierarchical RL framework that enables wheeled-legged robots to perform high-dynamic reflexive obstacle evasion with emergent gaits in simulation and on the real M20 platform.
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QuietWalk: Physics-Informed Reinforcement Learning for Ground Reaction Force-Aware Humanoid Locomotion Under Diverse Footwear
QuietWalk combines an inverse-dynamics-constrained PINN for GRF estimation with RL to produce low-impact humanoid locomotion policies that generalize across footwear, cutting mean noise by 7.17 dB on hardware.
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LatentMimic: Terrain-Adaptive Locomotion via Latent Space Imitation
LatentMimic decouples stylistic fidelity from geometric terrain constraints in quadruped locomotion via marginal latent divergence to a mocap prior and a dynamic replay buffer, yielding higher traversal success than motion-tracking baselines while preserving gait style.
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Attention-Based Neural-Augmented Kalman Filter for Legged Robot State Estimation
AttenNKF augments InEKF with an attention-based neural compensator trained in latent space to correct foot-slip errors in legged robot state estimation.
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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.
- CART: Context-Aware Terrain Adaptation using Temporal Sequence Selection for Legged Robots