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jax-backend【效率与协作】Use IKPy's optional JAX backend for CPU-safe JIT/AOT forward kinematics, autodiff Jacobians, and bounded SciPy inverse kinematics, with NumPy parity checks and explicit compilation and precision tradeoffs.
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Use this sub-skill when an already-constructed IKPy Chain needs JAX-backed FK
or IK. Install the optional extra first; JAX is not a base dependency and this
skill never promises GPU acceleration.
ikpy[jax] and verify ikpy.JAX_AVAILABLE in the same Python
environment that will run the chain.OriginLink and URDFLink chains (revolute, prismatic, or fixed); route
general chain construction and semantics to chain-kinematics.jax_precompile=True for predictable first-use latency or False
for cheap cache creation and lazy first-call compilation. Run FK once and
compare it with NumPy before using JAX IK.Chain.forward_kinematics(..., backend="jax") or
Chain.inverse_kinematics(..., backend="jax"). For a trajectory, pass the
previous full solution as initial_position.jax and jaxlib arrive through the optional jax extra:
python -m pip install 'ikpy[jax]'.JAX_PLATFORMS=cpu run is
the safe baseline.rtol=1e-5, atol=1e-5 (the repository's JAX tests'
baseline) before trusting a result. Use x64 only when configured before JAX
and cache creation.DHLink is not a supported JAX execution path: extraction labels it as a
separate type, but the JAX dispatch switch has no DH case. Unknown custom
link classes raise ValueError. Use NumPy or convert the model through the
appropriate importer.The bundled scripts/smoke_jax.py is a deterministic, inline-chain smoke
check. It supports --help, exits cleanly when JAX is absent, checks NumPy/JAX
FK parity, and can run bounded JAX IK with --ik without requiring a GPU.