https://github.com/Humanoid-SkillBlender/SkillBlender
공식 코드SkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation via Skill Blending
Papers with Code 아카이브
Humanoid Control
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Control of a high-dimensional humanoid. This can include skill learning by tracking motion capture clips, learning goal-directed tasks like going towards a moving target, and generating motion within a physics simulator.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Khrylx/RFC
공식 코드Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis
https://github.com/amathislab/kinesis
공식 코드https://github.com/brachiation-rl/brachiation
공식 코드https://github.com/facebookresearch/humenv
공식 코드Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models
https://github.com/facebookresearch/metamotivo
공식 코드Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models
https://github.com/facebookresearch/svg
공식 코드On the model-based stochastic value gradient for continuous reinforcement learning
https://github.com/google-deepmind/mujoco_mpc
공식 코드MuJoCo MPC for Humanoid Control: Evaluation on HumanoidBench
https://github.com/google-research/reincarnating_rl
공식 코드Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress
https://github.com/marikoisogawa/OpticalNLOSPose
공식 코드Optical Non-Line-of-Sight Physics-based 3D Human Pose Estimation
https://github.com/microsoft/MoCapAct
공식 코드MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control
https://github.com/nnaisense/pgpelib
공식 코드ClipUp: A Simple and Powerful Optimizer for Distribution-based Policy Evolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.