An equilibrium-propagation-based PPO controller for a 12-DoF quadruped achieves locomotion performance comparable to backpropagation-trained PPO on uneven terrain while using 4.3 times less GPU memory.
Learning to walk via deep reinforcement learning
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Integrating foot position maps into heightmaps and adding a locomotion-stability reward in an attention-based RL framework improves quadrupedal success rates on both trained and out-of-domain complex terrains.
citing papers explorer
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Neuromorphic Reinforcement Learning for Quadruped Locomotion Control on Uneven Terrain
An equilibrium-propagation-based PPO controller for a 12-DoF quadruped achieves locomotion performance comparable to backpropagation-trained PPO on uneven terrain while using 4.3 times less GPU memory.
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Learning Locomotion on Complex Terrain for Quadrupedal Robots with Foot Position Maps and Stability Rewards
Integrating foot position maps into heightmaps and adding a locomotion-stability reward in an attention-based RL framework improves quadrupedal success rates on both trained and out-of-domain complex terrains.