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Found 8 papers, 3 papers with code
Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation
EDH mitigates the divergence between labeled data of source subjects and unlabeled data of target subjects to accurately classify the locomotion modes of target subjects without labeling data.
Preserving Domain Private Representation via Mutual Information Maximization
Recent advances in unsupervised domain adaptation have shown that mitigating the domain divergence by extracting the domain-invariant representation could significantly improve the generalization of a model to an unlabeled data domain.
How does the structure embedded in learning policy affect learning quadruped locomotion?
This paper focuses on the latter because the structured policy is more intuitive and can inherit insights from previous model-based controllers.
Off-policy Maximum Entropy Reinforcement Learning : Soft Actor-Critic with Advantage Weighted Mixture Policy(SAC-AWMP)
A common way to solve this problem, known as Mixture-of-Experts, is to represent the policy as the weighted sum of multiple components, where different components perform well on different parts of the state space.
Teach Biped Robots to Walk via Gait Principles and Reinforcement Learning with Adversarial Critics
However, the local minima caused by unsuitable rewards and the overestimation of the cumulative reward impede the maximization of the cumulative reward.
Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features
Learning on point cloud is eagerly in demand because the point cloud is a common type of geometric data and can aid robots to understand environments robustly.
Directional PointNet: 3D Environmental Classification for Wearable Robotics
Environmental information can provide reliable prior information about human motion intent, which can aid the subject with wearable robotics to walk in complex environments.
Unsupervised Domain Adaptation Learning Algorithm for RGB-D Staircase Recognition
However, the performance of traditional ML techniques is limited by the amount of labeled RGB-D staircase data.
