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Legged Robot State Estimation With Invariant Extended Kalman Filter Using Neural Measurement Network
This paper introduces a novel proprioceptive state estimator for legged robots that combines model-based filters and deep neural networks.
Not Only Rewards But Also Constraints: Applications on Legged Robot Locomotion
In this work, we propose a novel reinforcement learning framework for training neural network controllers for complex robotic systems consisting of both rewards and constraints.
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