Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Use ASAP to train humanoid policies, retarget SMPL motions, and run sim2sim or sim2real deployment workflows for humanoidverse-based robots.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Use this skill for the ASAP humanoid learning stack: training and evaluating policies in HumanoidVerse, retargeting SMPL/AMASS motion into robot motion files, and running sim2sim or sim2real deployment helpers.
Start with the bundled doctor script from the generated ASAP skill root, then add the backend-specific pieces you need.
python scripts/asap_doctor.py --help
python scripts/asap_doctor.py --repo-root <asap-checkout> --section core
For the common Python stack, install the editable packages plus the repo-local utilities you need:
pip install -e .
pip install -e isaac_utils
# optional for deployment helpers
pip install -e sim2real
Use a CUDA-capable PyTorch build for the training routes and the policy runtime routes that expect GPU acceleration. Install simulator- or hardware-specific SDKs separately when the doctor script reports them missing.
Read references/install-and-backends.md before choosing a simulator backend or attempting motion retargeting or robot deployment.
sub-skills/training-and-evaluation/SKILL.md for motion-tracking, locomotion, delta-action, checkpoint, and Hydra-config workflows.sub-skills/motion-retargeting/SKILL.md for shape fitting, motion fitting, asset validation, and MuJoCo visualization.sub-skills/sim2real-deployment/SKILL.md for MuJoCo playback, ROS2/Unitree bridges, policy toggles, joystick control, and data logging.scripts/asap_doctor.py first.references/troubleshooting.md before editing commands.references/repo-provenance.md to compare the checkout against the generated skill snapshot.references/configuration-map.md and then route to the relevant sub-skill.references/configuration-map.md — Hydra groups, common overrides, and output-directory conventions.references/install-and-backends.md — base package install, simulator backends, and external SDK prerequisites.references/troubleshooting.md — import, backend, config, and runtime failure patterns.references/repo-provenance.md — source commit and evidence baseline for refresh decisions.references/repo-routing-metadata.json — structured router placement metadata for import into repo-skills-router.This is a runtime skill for the ASAP package, not a maintainer release guide. It does not cover unrelated repo maintenance, packaging policy, or non-ASAP robot stacks.
If a task is only about generic PyTorch, general RL theory, or a different simulator stack, route it elsewhere instead of forcing it through ASAP-specific guidance.
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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
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Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
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Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
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Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
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Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
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