PriPG-RL trains RL policies for POMDPs by distilling knowledge from a privileged anytime-feasible MPC planner into a P2P-SAC policy, improving sample efficiency and performance in partially observable robotic navigation.
Learning quadrupedal locomotion over challenging terrain
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4representative citing papers
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.
citing papers explorer
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PriPG-RL: Privileged Planner-Guided Reinforcement Learning for Partially Observable Systems with Anytime-Feasible MPC
PriPG-RL trains RL policies for POMDPs by distilling knowledge from a privileged anytime-feasible MPC planner into a P2P-SAC policy, improving sample efficiency and performance in partially observable robotic navigation.
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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.
- CART: Context-Aware Terrain Adaptation using Temporal Sequence Selection for Legged Robots