jaxsim
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Published
20 hours ago •
ami-iit
ami-iit
Error when passing two different pytrees with the same structure to a JIT-compiled function
Also with the new functional APIs, the following problem still affects our code:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
A MWE to reproduce the problem is the following:
import jax.numpy as jnp
import jaxsim.api as js
import rod.builder.primitives
import rod.urdf.exporter
# Create on-the-fly a ROD model of a box.
rod_model = (
rod.builder.primitives.BoxBuilder(x=0.3, y=0.2, z=0.1, mass=1.0, name="box")
.build_model()
.add_link()
.add_inertial()
.add_visual()
.add_collision()
.build()
)
# Export the URDF string.
urdf_string = rod.urdf.exporter.UrdfExporter.sdf_to_urdf_string(
sdf=rod_model, pretty=True
)
model1 = js.model.JaxSimModel.build_from_model_description(
model_description=urdf_string,
gravity=jnp.array([0, 0, -10]),
is_urdf=True,
)
model2 = js.model.JaxSimModel.build_from_model_description(
model_description=urdf_string,
gravity=jnp.array([0, 0, -10]),
is_urdf=True,
)
# This works
_ = js.contact.estimate_good_soft_contacts_parameters(model=model1)
# This raises
_ = js.contact.estimate_good_soft_contacts_parameters(model=model2)
I've already tried to play a bit with JaxSimModel.__eq__ and JaxSimModel.__hash__ with no luck.
Marking this issue as help wanted in case this problem becomes annoying to someone.
Interestingly, running it in a context in which jax.jit is disabled, works fine:
with jax.disable_jit():
_ = js.contact.estimate_good_soft_contacts_parameters(model=model1)
_ = js.contact.estimate_good_soft_contacts_parameters(model=model2)
I suspect that there is something strange in the jax cache that we are either overlooking or not handling properly.
This is basically the updated version of the problem occurring in the previous OOP APIs in #84 cc @flferretti
The example is failing with the following error for me:
traversaro@IITBMP014LW012:~/jaxsimws$ pixi run python mwe103.py
jaxsim[21436] INFO Enabling JAX to use 64bit precision
An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.
Traceback (most recent call last):
File "/home/traversaro/jaxsimws/mwe103.py", line 18, in <module>
urdf_string = rod.urdf.exporter.UrdfExporter.sdf_to_urdf_string(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/traversaro/jaxsimws/.pixi/envs/default/lib/python3.12/site-packages/rod/urdf/exporter.py", line 30, in sdf_to_urdf_string
model = sdf.models()[0]
~~~~~~~~~~~~^^^
IndexError: list index out of range
Installed packages:
Click
traversaro@IITBMP014LW012:~/jaxsimws$ pixi list
Package Version Build Size Kind Source
_libgcc_mutex 0.1 conda_forge 2.5 KiB conda _libgcc_mutex-0.1-conda_forge.tar.bz2
_openmp_mutex 4.5 2_gnu 23.1 KiB conda _openmp_mutex-4.5-2_gnu.tar.bz2
_sysroot_linux-64_curr_repodata_hack 3 h69a702a_14 20.7 KiB conda _sysroot_linux-64_curr_repodata_hack-3-h69a702a_14.conda
absl-py 2.1.0 pyhd8ed1ab_0 104.8 KiB conda absl-py-2.1.0-pyhd8ed1ab_0.conda
alsa-lib 1.2.11 hd590300_1 541.7 KiB conda alsa-lib-1.2.11-hd590300_1.conda
ampl-mp 3.1.0 h2cc385e_1006 1.1 MiB conda ampl-mp-3.1.0-h2cc385e_1006.tar.bz2
aom 3.8.1 h59595ed_0 2.6 MiB conda aom-3.8.1-h59595ed_0.conda
assimp 5.3.1 hfb0e8fe_2 3.4 MiB conda assimp-5.3.1-hfb0e8fe_2.conda
asttokens 2.4.1 pyhd8ed1ab_0 28.2 KiB conda asttokens-2.4.1-pyhd8ed1ab_0.conda
atk-1.0 2.38.0 hd4edc92_1 539 KiB conda atk-1.0-2.38.0-hd4edc92_1.tar.bz2
attr 2.5.1 h166bdaf_1 69.4 KiB conda attr-2.5.1-h166bdaf_1.tar.bz2
aws-c-auth 0.7.16 h79b3bcb_6 101 KiB conda aws-c-auth-0.7.16-h79b3bcb_6.conda
aws-c-cal 0.6.10 hb29e0c7_1 53.8 KiB conda aws-c-cal-0.6.10-hb29e0c7_1.conda
aws-c-common 0.9.13 hd590300_0 220.3 KiB conda aws-c-common-0.9.13-hd590300_0.conda
aws-c-compression 0.2.18 hecc5fa9_1 18.7 KiB conda aws-c-compression-0.2.18-hecc5fa9_1.conda
aws-c-event-stream 0.4.2 hf9b2f7b_4 52.7 KiB conda aws-c-event-stream-0.4.2-hf9b2f7b_4.conda
aws-c-http 0.8.1 h5d7533a_5 190.5 KiB conda aws-c-http-0.8.1-h5d7533a_5.conda
aws-c-io 0.14.5 h50678d4_1 153.5 KiB conda aws-c-io-0.14.5-h50678d4_1.conda
aws-c-mqtt 0.10.2 hf479d2b_4 160.6 KiB conda aws-c-mqtt-0.10.2-hf479d2b_4.conda
aws-c-s3 0.5.2 h4ad9680_0 102.8 KiB conda aws-c-s3-0.5.2-h4ad9680_0.conda
aws-c-sdkutils 0.1.15 hecc5fa9_1 54.1 KiB conda aws-c-sdkutils-0.1.15-hecc5fa9_1.conda
aws-checksums 0.1.18 hecc5fa9_1 49 KiB conda aws-checksums-0.1.18-hecc5fa9_1.conda
aws-crt-cpp 0.26.2 h19f5d62_7 326 KiB conda aws-crt-cpp-0.26.2-h19f5d62_7.conda
aws-sdk-cpp 1.11.267 h5606698_1 3.4 MiB conda aws-sdk-cpp-1.11.267-h5606698_1.conda
azure-core-cpp 1.11.1 h91d86a7_1 334.6 KiB conda azure-core-cpp-1.11.1-h91d86a7_1.conda
azure-storage-blobs-cpp 12.10.0 h00ab1b0_1 505 KiB conda azure-storage-blobs-cpp-12.10.0-h00ab1b0_1.conda
azure-storage-common-cpp 12.5.0 h94269e2_4 129.3 KiB conda azure-storage-common-cpp-12.5.0-h94269e2_4.conda
binutils_impl_linux-64 2.40 hf600244_0 5.2 MiB conda binutils_impl_linux-64-2.40-hf600244_0.conda
binutils_linux-64 2.40 hbdbef99_2 27.5 KiB conda binutils_linux-64-2.40-hbdbef99_2.conda
blosc 1.21.5 h0f2a231_0 47.6 KiB conda blosc-1.21.5-h0f2a231_0.conda
brotli 1.1.0 hd590300_1 18.9 KiB conda brotli-1.1.0-hd590300_1.conda
brotli-bin 1.1.0 hd590300_1 18.5 KiB conda brotli-bin-1.1.0-hd590300_1.conda
bullet-cpp 3.25 hfb8ada1_2 40.4 MiB conda bullet-cpp-3.25-hfb8ada1_2.conda
bzip2 1.0.8 hd590300_5 248.3 KiB conda bzip2-1.0.8-hd590300_5.conda
c-ares 1.27.0 hd590300_0 159.7 KiB conda c-ares-1.27.0-hd590300_0.conda
ca-certificates 2024.2.2 hbcca054_0 151.8 KiB conda ca-certificates-2024.2.2-hbcca054_0.conda
cairo 1.18.0 h3faef2a_0 959.3 KiB conda cairo-1.18.0-h3faef2a_0.conda
certifi 2024.2.2 pyhd8ed1ab_0 156.8 KiB conda certifi-2024.2.2-pyhd8ed1ab_0.conda
cfitsio 4.3.1 hbdc6101_0 854.7 KiB conda cfitsio-4.3.1-hbdc6101_0.conda
colorama 0.4.6 pyhd8ed1ab_0 24.6 KiB conda colorama-0.4.6-pyhd8ed1ab_0.tar.bz2
coloredlogs 15.0.1 pyhd8ed1ab_3 39.6 KiB conda coloredlogs-15.0.1-pyhd8ed1ab_3.tar.bz2
console_bridge 1.0.2 h924138e_1 18 KiB conda console_bridge-1.0.2-h924138e_1.tar.bz2
contourpy 1.2.0 py312h8572e83_0 247.2 KiB conda contourpy-1.2.0-py312h8572e83_0.conda
cppzmq 4.10.0 h2e2a08d_1 28.4 KiB conda cppzmq-4.10.0-h2e2a08d_1.conda
cuda-cccl_linux-64 12.4.99 ha770c72_0 1.2 MiB conda cuda-cccl_linux-64-12.4.99-ha770c72_0.conda
cuda-crt-dev_linux-64 12.4.99 ha770c72_0 86.2 KiB conda cuda-crt-dev_linux-64-12.4.99-ha770c72_0.conda
cuda-crt-tools 12.4.99 ha770c72_0 25.5 KiB conda cuda-crt-tools-12.4.99-ha770c72_0.conda
cuda-cudart 12.4.99 hd3aeb46_0 21.9 KiB conda cuda-cudart-12.4.99-hd3aeb46_0.conda
cuda-cudart-dev 12.4.99 hd3aeb46_0 22.4 KiB conda cuda-cudart-dev-12.4.99-hd3aeb46_0.conda
cuda-cudart-dev_linux-64 12.4.99 h59595ed_0 351.7 KiB conda cuda-cudart-dev_linux-64-12.4.99-h59595ed_0.conda
cuda-cudart-static 12.4.99 hd3aeb46_0 22 KiB conda cuda-cudart-static-12.4.99-hd3aeb46_0.conda
cuda-cudart-static_linux-64 12.4.99 h59595ed_0 724.8 KiB conda cuda-cudart-static_linux-64-12.4.99-h59595ed_0.conda
cuda-cudart_linux-64 12.4.99 h59595ed_0 182.3 KiB conda cuda-cudart_linux-64-12.4.99-h59595ed_0.conda
cuda-driver-dev_linux-64 12.4.99 h59595ed_0 35.2 KiB conda cuda-driver-dev_linux-64-12.4.99-h59595ed_0.conda
cuda-nvcc 12.4.99 hcdd1206_0 22.8 KiB conda cuda-nvcc-12.4.99-hcdd1206_0.conda
cuda-nvcc-dev_linux-64 12.4.99 ha770c72_0 10.7 MiB conda cuda-nvcc-dev_linux-64-12.4.99-ha770c72_0.conda
cuda-nvcc-impl 12.4.99 hd3aeb46_0 23.8 KiB conda cuda-nvcc-impl-12.4.99-hd3aeb46_0.conda
cuda-nvcc-tools 12.4.99 hd3aeb46_0 22.7 MiB conda cuda-nvcc-tools-12.4.99-hd3aeb46_0.conda
cuda-nvcc_linux-64 12.4.99 h8a487aa_0 24.5 KiB conda cuda-nvcc_linux-64-12.4.99-h8a487aa_0.conda
cuda-nvvm-dev_linux-64 12.4.99 ha770c72_0 23.5 KiB conda cuda-nvvm-dev_linux-64-12.4.99-ha770c72_0.conda
cuda-nvvm-impl 12.4.99 h59595ed_0 8.6 MiB conda cuda-nvvm-impl-12.4.99-h59595ed_0.conda
cuda-nvvm-tools 12.4.99 h59595ed_0 11.2 MiB conda cuda-nvvm-tools-12.4.99-h59595ed_0.conda
cuda-version 12.4 h3060b56_3 20.5 KiB conda cuda-version-12.4-h3060b56_3.conda
cycler 0.12.1 pyhd8ed1ab_0 13.1 KiB conda cycler-0.12.1-pyhd8ed1ab_0.conda
dartsim 6.13.1 hdbb2bd4_2 13.6 MiB conda dartsim-6.13.1-hdbb2bd4_2.conda
dav1d 1.2.1 hd590300_0 742.4 KiB conda dav1d-1.2.1-hd590300_0.conda
dbus 1.13.6 h5008d03_3 604.1 KiB conda dbus-1.13.6-h5008d03_3.tar.bz2
decorator 5.1.1 pyhd8ed1ab_0 11.8 KiB conda decorator-5.1.1-pyhd8ed1ab_0.tar.bz2
docstring_parser 0.15 pyhd8ed1ab_0 29.3 KiB conda docstring_parser-0.15-pyhd8ed1ab_0.conda
eigen 3.4.0 h00ab1b0_0 1 MiB conda eigen-3.4.0-h00ab1b0_0.conda
etils 1.7.0 pyhd8ed1ab_0 758.4 KiB conda etils-1.7.0-pyhd8ed1ab_0.conda
exceptiongroup 1.2.0 pyhd8ed1ab_2 20.1 KiB conda exceptiongroup-1.2.0-pyhd8ed1ab_2.conda
executing 2.0.1 pyhd8ed1ab_0 27 KiB conda executing-2.0.1-pyhd8ed1ab_0.conda
expat 2.6.1 h59595ed_0 133.6 KiB conda expat-2.6.1-h59595ed_0.conda
fcl 0.7.0 hadc09e8_4 1.5 MiB conda fcl-0.7.0-hadc09e8_4.conda
ffmpeg 6.1.1 gpl_h8007c5b_104 9.3 MiB conda ffmpeg-6.1.1-gpl_h8007c5b_104.conda
flann 1.9.2 h2b5ea80_0 1.5 MiB conda flann-1.9.2-h2b5ea80_0.conda
fmt 10.2.1 h00ab1b0_0 189.3 KiB conda fmt-10.2.1-h00ab1b0_0.conda
font-ttf-dejavu-sans-mono 2.37 hab24e00_0 388.1 KiB conda font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
font-ttf-inconsolata 3.000 h77eed37_0 94.3 KiB conda font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
font-ttf-source-code-pro 2.038 h77eed37_0 684.4 KiB conda font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
font-ttf-ubuntu 0.83 h77eed37_1 1.5 MiB conda font-ttf-ubuntu-0.83-h77eed37_1.conda
fontconfig 2.14.2 h14ed4e7_0 265.6 KiB conda fontconfig-2.14.2-h14ed4e7_0.conda
fonts-conda-ecosystem 1 0 3.6 KiB conda fonts-conda-ecosystem-1-0.tar.bz2
fonts-conda-forge 1 0 4 KiB conda fonts-conda-forge-1-0.tar.bz2
fonttools 4.49.0 py312h98912ed_0 2.7 MiB conda fonttools-4.49.0-py312h98912ed_0.conda
freeglut 3.2.2 hac7e632_2 139.6 KiB conda freeglut-3.2.2-hac7e632_2.conda
freeimage 3.18.0 h4b96d29_20 450.6 KiB conda freeimage-3.18.0-h4b96d29_20.conda
freetype 2.12.1 h267a509_2 620.1 KiB conda freetype-2.12.1-h267a509_2.conda
freexl 2.0.0 h743c826_0 58.4 KiB conda freexl-2.0.0-h743c826_0.conda
fribidi 1.0.10 h36c2ea0_0 111.7 KiB conda fribidi-1.0.10-h36c2ea0_0.tar.bz2
frozendict 2.4.0 py312h98912ed_0 29.4 KiB conda frozendict-2.4.0-py312h98912ed_0.conda
fsspec 2024.2.0 pyhca7485f_0 125.6 KiB conda fsspec-2024.2.0-pyhca7485f_0.conda
gazebo 11.14.0 h3102449_8 57 MiB conda gazebo-11.14.0-h3102449_8.conda
gcc_impl_linux-64 12.3.0 he2b93b0_5 49.5 MiB conda gcc_impl_linux-64-12.3.0-he2b93b0_5.conda
gcc_linux-64 12.3.0 h76fc315_2 29.6 KiB conda gcc_linux-64-12.3.0-h76fc315_2.conda
gdbm 1.18 h0a1914f_2 190.2 KiB conda gdbm-1.18-h0a1914f_2.tar.bz2
gdk-pixbuf 2.42.10 h829c605_4 558.6 KiB conda gdk-pixbuf-2.42.10-h829c605_4.conda
geos 3.12.1 h59595ed_0 1.7 MiB conda geos-3.12.1-h59595ed_0.conda
geotiff 1.7.1 h6b2125f_15 130 KiB conda geotiff-1.7.1-h6b2125f_15.conda
gettext 0.21.1 h27087fc_0 4.1 MiB conda gettext-0.21.1-h27087fc_0.tar.bz2
giflib 5.2.1 h0b41bf4_3 75.6 KiB conda giflib-5.2.1-h0b41bf4_3.conda
gitdb 4.0.11 pyhd8ed1ab_0 51.6 KiB conda gitdb-4.0.11-pyhd8ed1ab_0.conda
gitpython 3.1.42 pyhd8ed1ab_0 146.1 KiB conda gitpython-3.1.42-pyhd8ed1ab_0.conda
glfw 3.4 hd590300_0 163.5 KiB conda glfw-3.4-hd590300_0.conda
glib 2.78.4 hfc55251_4 478.5 KiB conda glib-2.78.4-hfc55251_4.conda
glib-tools 2.78.4 hfc55251_4 110.4 KiB conda glib-tools-2.78.4-hfc55251_4.conda
gmp 6.3.0 h59595ed_0 549.9 KiB conda gmp-6.3.0-h59595ed_0.conda
gnutls 3.7.9 hb077bed_0 1.9 MiB conda gnutls-3.7.9-hb077bed_0.conda
graphite2 1.3.13 h58526e2_1001 102.2 KiB conda graphite2-1.3.13-h58526e2_1001.tar.bz2
graphviz 9.0.0 h78e8752_1 2.2 MiB conda graphviz-9.0.0-h78e8752_1.conda
gst-plugins-base 1.22.9 h8e1006c_0 2.6 MiB conda gst-plugins-base-1.22.9-h8e1006c_0.conda
gstreamer 1.22.9 h98fc4e7_0 1.9 MiB conda gstreamer-1.22.9-h98fc4e7_0.conda
gtk2 2.24.33 h7f000aa_3 6.2 MiB conda gtk2-2.24.33-h7f000aa_3.conda
gts 0.7.6 h977cf35_4 310.9 KiB conda gts-0.7.6-h977cf35_4.conda
gxx_impl_linux-64 12.3.0 he2b93b0_5 12.2 MiB conda gxx_impl_linux-64-12.3.0-he2b93b0_5.conda
gxx_linux-64 12.3.0 h8a814eb_2 28 KiB conda gxx_linux-64-12.3.0-h8a814eb_2.conda
harfbuzz 8.3.0 h3d44ed6_0 1.5 MiB conda harfbuzz-8.3.0-h3d44ed6_0.conda
hdf4 4.2.15 h2a13503_7 739 KiB conda hdf4-4.2.15-h2a13503_7.conda
hdf5 1.14.3 nompi_h4f84152_100 3.7 MiB conda hdf5-1.14.3-nompi_h4f84152_100.conda
humanfriendly 10.0 pyhd8ed1ab_6 71.7 KiB conda humanfriendly-10.0-pyhd8ed1ab_6.conda
icdiff 2.0.7 pyhd8ed1ab_0 24.7 KiB conda icdiff-2.0.7-pyhd8ed1ab_0.conda
icu 73.2 h59595ed_0 11.5 MiB conda icu-73.2-h59595ed_0.conda
idyntree 10.3.0 py312h36ac6ef_0 2.3 MiB conda idyntree-10.3.0-py312h36ac6ef_0.conda
imath 3.1.11 hfc55251_0 158.7 KiB conda imath-3.1.11-hfc55251_0.conda
importlib-metadata 7.0.2 pyha770c72_0 26.3 KiB conda importlib-metadata-7.0.2-pyha770c72_0.conda
importlib_metadata 7.0.2 hd8ed1ab_0 9.2 KiB conda importlib_metadata-7.0.2-hd8ed1ab_0.conda
importlib_resources 6.1.3 pyhd8ed1ab_0 29.9 KiB conda importlib_resources-6.1.3-pyhd8ed1ab_0.conda
iniconfig 2.0.0 pyhd8ed1ab_0 10.8 KiB conda iniconfig-2.0.0-pyhd8ed1ab_0.conda
ipopt 3.14.14 h04b96a2_1 996.7 KiB conda ipopt-3.14.14-h04b96a2_1.conda
ipython 8.22.2 pyh707e725_0 579.8 KiB conda ipython-8.22.2-pyh707e725_0.conda
irrlicht 1.8.5 h2a6caf8_4 1.9 MiB conda irrlicht-1.8.5-h2a6caf8_4.conda
jax 0.4.25 pyhd8ed1ab_0 1.1 MiB conda jax-0.4.25-pyhd8ed1ab_0.conda
jax-dataclasses 1.6.0 pyhd8ed1ab_0 18 KiB conda jax-dataclasses-1.6.0-pyhd8ed1ab_0.conda
jaxlib 0.4.23 cpu_py312h1743843_0 52.3 MiB conda jaxlib-0.4.23-cpu_py312h1743843_0.conda
jaxlie 1.3.4 pyhd8ed1ab_0 21.5 KiB conda jaxlie-1.3.4-pyhd8ed1ab_0.conda
jedi 0.19.1 pyhd8ed1ab_0 821.6 KiB conda jedi-0.19.1-pyhd8ed1ab_0.conda
json-c 0.17 h7ab15ed_0 81.1 KiB conda json-c-0.17-h7ab15ed_0.conda
jsoncpp 1.9.5 h4bd325d_1 190 KiB conda jsoncpp-1.9.5-h4bd325d_1.tar.bz2
jxrlib 1.1 hd590300_3 233.5 KiB conda jxrlib-1.1-hd590300_3.conda
kealib 1.5.3 h2f55d51_0 168.9 KiB conda kealib-1.5.3-h2f55d51_0.conda
kernel-headers_linux-64 4.18.0 he073ed8_1 1.2 MiB conda kernel-headers_linux-64-4.18.0-he073ed8_1.conda
keyutils 1.6.1 h166bdaf_0 115.1 KiB conda keyutils-1.6.1-h166bdaf_0.tar.bz2
kiwisolver 1.4.5 py312h8572e83_1 70.4 KiB conda kiwisolver-1.4.5-py312h8572e83_1.conda
krb5 1.21.2 h659d440_0 1.3 MiB conda krb5-1.21.2-h659d440_0.conda
lame 3.100 h166bdaf_1003 496.3 KiB conda lame-3.100-h166bdaf_1003.tar.bz2
lcms2 2.16 hb7c19ff_0 239.5 KiB conda lcms2-2.16-hb7c19ff_0.conda
ld_impl_linux-64 2.40 h41732ed_0 688.2 KiB conda ld_impl_linux-64-2.40-h41732ed_0.conda
lerc 4.0.0 h27087fc_0 275.2 KiB conda lerc-4.0.0-h27087fc_0.tar.bz2
libabseil 20240116.1 cxx17_h59595ed_2 1.2 MiB conda libabseil-20240116.1-cxx17_h59595ed_2.conda
libaec 1.1.2 h59595ed_1 34.4 KiB conda libaec-1.1.2-h59595ed_1.conda
libarchive 3.7.2 h2aa1ff5_1 845.9 KiB conda libarchive-3.7.2-h2aa1ff5_1.conda
libass 0.17.1 h8fe9dca_1 123.9 KiB conda libass-0.17.1-h8fe9dca_1.conda
libblas 3.9.0 21_linux64_openblas 14.3 KiB conda libblas-3.9.0-21_linux64_openblas.conda
libboost 1.82.0 h6fcfa73_6 2.5 MiB conda libboost-1.82.0-h6fcfa73_6.conda
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tk 8.6.13 noxft_h4845f30_101 3.2 MiB conda tk-8.6.13-noxft_h4845f30_101.conda
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wayland 1.22.0 h8c25dac_1 299.3 KiB conda wayland-1.22.0-h8c25dac_1.conda
wcwidth 0.2.13 pyhd8ed1ab_0 31.9 KiB conda wcwidth-0.2.13-pyhd8ed1ab_0.conda
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xcb-util-keysyms 0.4.0 h8ee46fc_1 13.9 KiB conda xcb-util-keysyms-0.4.0-h8ee46fc_1.conda
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xorg-libxt 1.3.0 hd590300_1 370.4 KiB conda xorg-libxt-1.3.0-hd590300_1.conda
xorg-renderproto 0.11.1 h7f98852_1002 9.4 KiB conda xorg-renderproto-0.11.1-h7f98852_1002.tar.bz2
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xz 5.2.6 h166bdaf_0 408.6 KiB conda xz-5.2.6-h166bdaf_0.tar.bz2
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Perhaps a specific rod version is required?
I guess it needs https://github.com/ami-iit/rod/pull/27/commits/0ce60b22e18432f6a557e83f2a1100cb0bd4cdd4 included in https://github.com/ami-iit/rod/pull/27.
Also #83 actually needed https://github.com/ami-iit/rod/pull/27 for proper color support, we need to bump the dependencies before releasing jaxsim v0.2.0.
Playing a bit around, I was able to reduce the MWE, to remove jaxsim at all:
import dataclasses
import jax
import jax.numpy as jnp
import jax_dataclasses
from jax_dataclasses import Static
import pathlib
import numpy.typing as npt
import dataclasses
from typing import Dict, Union
import jax.lax
import jax.numpy as jnp
import jax_dataclasses
import numpy as np
from jax_dataclasses import Static
@jax_dataclasses.pytree_dataclass
class MWE103GroundContact:
body: Static[npt.NDArray] = dataclasses.field(
default_factory=lambda: np.array([])
)
@staticmethod
def build_from() -> "MWE103GroundContact":
# Build the object
gc = MWE103GroundContact(body=np.zeros(8))
return gc
def local_total_mass(model: MWE103GroundContact) -> float:
return 1.0
dummy_contact1 = MWE103GroundContact.build_from()
dummy_contact2 = MWE103GroundContact.build_from()
local_total_mass_jit1 = jax.jit(local_total_mass)
local_total_mass_jit2 = jax.jit(local_total_mass)
_ = local_total_mass_jit1(model=dummy_contact1)
_ = local_total_mass_jit2(model=dummy_contact2)
Somehow the problem is related to the body attribute of the GroundContact class: https://github.com/ami-iit/jaxsim/blob/f3d9abb0dd8583c5391db7b47c998ad0a457adea/src/jaxsim/physics/model/ground_contact.py#L26 . Commenting it, the problem disappears.
Somehow the problem is related to the
bodyattribute of theGroundContactclass:
This is aligned with what was found in https://github.com/ami-iit/jaxsim/issues/84#issuecomment-1943719169 .
I think the answer to this question clarifies everything: https://github.com/google/jax/issues/19547#issuecomment-1913711588 .
I think I described the root issue behind all the ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() in https://github.com/ami-iit/jaxsim/pull/105 .
Doing some additional tests, I found out that a similar problem is raised. This time, it seems to be related to the integrators:
Traceback (most recent call last):
File "/home/flferretti/git/comodo/src/comodo/jaxsimSimulator/test.py", line 55, in <module>
integrator_state2 = integrator2.init(x0=data2.state, t0=0, dt=1e-3)
File "/home/flferretti/jaxsim/src/jaxsim/integrators/common.py", line 166, in init
_ = integrator(x0, t0, dt, **kwargs)
File "/home/flferretti/jaxsim/src/jaxsim/integrators/common.py", line 283, in __call__
z = self._compute_next_state(x0=x0, t0=t0, dt=dt, **kwargs)
File "/home/flferretti/jaxsim/src/jaxsim/integrators/common.py", line 417, in _compute_next_state
K, _ = jax.lax.scan(
File "/home/flferretti/jaxsim/src/jaxsim/integrators/common.py", line 403, in scan_body
ki = jax.lax.cond(
File "/home/flferretti/jaxsim/src/jaxsim/integrators/common.py", line 374, in <lambda>
get_ẋ0 = lambda: self.params.get("dxdt0", f(x0, t0)[0])
File "/home/flferretti/jaxsim/src/jaxsim/integrators/common.py", line 365, in <lambda>
f = lambda x, t: self.dynamics(x=x, t=t, **kwargs)
File "/home/flferretti/jaxsim/src/jaxsim/api/ode.py", line 69, in f
return system_dynamics(
jax.errors.TracerBoolConversionError: Attempted boolean conversion of traced array with shape bool[1]..
The error occurred while tracing the function <lambda> at /home/flferretti/jaxsim/src/jaxsim/integrators/common.py:374 for cond. This value became a tracer due to JAX operations on these lines:
operation a:bool[1] = eq b c
from line /home/flferretti/jaxsim/src/jaxsim/api/ode.py:69 (f)
See https://jax.readthedocs.io/en/latest/errors.html#jax.errors.TracerBoolConversionError
MWE
import jax.numpy as jnp
import jaxsim.api as js
import rod.builder.primitives
import rod.urdf.exporter
from jaxsim import integrators
# Create on-the-fly a ROD model of a box.
rod_model = (
rod.builder.primitives.BoxBuilder(x=0.3, y=0.2, z=0.1, mass=1.0, name="box")
.build_model()
.add_link()
.add_inertial()
.add_visual()
.add_collision()
.build()
)
# Export the URDF string.
urdf_string = rod.urdf.exporter.UrdfExporter.sdf_to_urdf_string(
sdf=rod_model, pretty=True
)
model1 = js.model.JaxSimModel.build_from_model_description(
model_description=urdf_string,
is_urdf=True,
)
model2 = js.model.JaxSimModel.build_from_model_description(
model_description=urdf_string,
is_urdf=True,
)
# Build the data
data1 = js.data.JaxSimModelData.build(model=model1)
data2 = js.data.JaxSimModelData.build(model=model2)
# Create the integrators
integrator1 = integrators.fixed_step.Heun2SO3.build(
dynamics=js.ode.wrap_system_dynamics_for_integration(
model=model1,
data=data1,
system_dynamics=js.ode.system_dynamics,
),
)
integrator2 = integrators.fixed_step.Heun2SO3.build(
dynamics=js.ode.wrap_system_dynamics_for_integration(
model=model2,
data=data2,
system_dynamics=js.ode.system_dynamics,
),
)
# ! Try to initialize the integrator
integrator_state1 = integrator1.init(x0=data1.state, t0=0, dt=1e-3)
integrator_state2 = integrator2.init(x0=data2.state, t0=0, dt=1e-3)
After some testing, I found out that the objects data.state.physics_model are not compatible. Moreover, when data is created for a model without joints, like in the example in https://github.com/ami-iit/jaxsim/issues/103#issuecomment-2058752595, the fact that the attributes joint_positions and joint_velocities are empty raises https://github.com/google/jax/blob/95c05521b457f2572b0704449c76982c85ee72b6/jax/_src/core.py#L646-L647.
Setting hash=False for both attributes partially solves the problem, but still I cannot compare data1 with data2 to get a boolean
Furthermore, I believe that we should pay attention to the dynamics attribute of Integrator, which being just a Protocol, it cannot be compared with ==
Thanks for the investigation @flferretti. Since JaxSimModelData contains many JAX arrays as attributes, I believe that the __hash__ and __eq__ methods need to be properly implemented. I'll have a look at it, I've already have some WIP work on the implementation of these methods for the jaxsim.parser package, I'll extend it also to JaxSimModelData.