Hello Harry,
I tried to replicate the RL example in the GitHub link you shared,
but I have been trying to fix the following error. It seems the
pychrono.parsers module is missing.
Traceback (most recent call last):
File
"C:\Users\chike\Box\TurtleRobotExperiments\Sea_Turtle_Robot_AI_Powered_Simulations_Project\NnamdiFiles\mujocotest1\chrono\multi-terrain-RL\rl_examples\rslrl\eval.py",
line 8, in <module>
from chrono_env import ChronoQuadrupedEnv as RigidTerrainEnv
File
"C:\Users\chike\Box\TurtleRobotExperiments\Sea_Turtle_Robot_AI_Powered_Simulations_Project\NnamdiFiles\mujocotest1\chrono\multi-terrain-RL\rl_examples\rslrl\chrono_env.py",
line 13, in <module>
import simulation.Robots as Robots
File
"C:\Users\chike\Box\TurtleRobotExperiments\Sea_Turtle_Robot_AI_Powered_Simulations_Project\NnamdiFiles\mujocotest1\chrono\multi-terrain-RL\simulation\Robots.py",
line 7, in <module>
import pychrono.parsers as parsers
ModuleNotFoundError: No module named 'pychrono.parsers'
Could you please provide some help with this? I followed
conda create -n chrono "python<3.13" -c conda-forge conda activate
chrono conda install bochengzou::pychrono -c bochengzou -c nvidia -c
dlr-sc -c conda-forge
Install RL Library
Inside the conda environment, supposingly named as chrono:
pip install rsl-rl-lib==2.2.4
Verify Installation
Inside the conda environment:
cd <path_to_current_folder>/multi-terrain-RL python
rl_examples/rslrl/eval.py --ckpt 2999
Best regards,
Nnamdi
On Monday, August 4, 2025 at 3:57:30 PM UTC-4 [email protected] wrote:
Hello Nnamdi,
I hope you are doing well.
I want to share this repo
(https://github.com/uwsbel/sbel-reproducibility/tree/master/2025/multi-terrain-RL
<https://urldefense.com/v3/__https://github.com/uwsbel/sbel-reproducibility/tree/master/2025/multi-terrain-RL__;!!Mak6IKo!NNZn_b2z9W4LO4J581E-MjNPOiP3Ycu6Aq4Tl1QDCtM_LLuFYDaL6-RQ1C6nAeNfanFjXrRNIOIfwQNCC_e1hx3XANFT5w$>)
with you, which does the RL training for the demo videos I shared
earlier. The specific robot type or task might differ, but I
think the structure or idea would be similar.
I realize I previously offered to meet, but after giving it more
thought—and in light of similar requests from others—We’ve
decided it's better to share a prepared code repo instead. This
way, you can try it on your own and post any questions here, so
others can also benefit from the discussion.
Best,
Harry
On Wednesday, July 30, 2025 at 7:50:49 PM UTC-5 [email protected] wrote:
Hello Harry,
Your simulation results looks great and are similar to what I
hope to implement in my setup. I am interested in discussing
more about this and would love to set up a meeting as soon as
possible. Can you please let me know what day/time you are
available?
Best regards,
Nnamdi
On Wednesday, July 30, 2025 at 1:50:56 PM UTC-4
[email protected] wrote:
Picture attachment for last message:
Screenshot from 2025-07-30 10-45-41.png
Best,
Harry
On Wednesday, July 30, 2025 at 11:17:42 AM UTC-5 Harry
ZHANG wrote:
Hello Nnamdi,
My name's Harry and Dan asked me to follow up on this
thread.
About couple weeks ago, we start exploring using
PyChrono as our physics engine to train RL policies
for quadruped locomotion task. Our projects's goal is
to investigate simulation terrain's effect on
locomotion policy performance. In PyChrono there are
different fidelity levels for terrain model (from low
to high): rigid terrain (rtf<<1) --> SCM deformable
terrain (rtf~=1)-->CRM granular terrain(rtf~=5),
where rtf means real time fator. RL library we are
using is "RSL-RL".
Please check this link for prelim results we got:
https://drive.google.com/drive/folders/1Yhx1goYDsv0FZdk4OfyH9_yRazkRSwMQ?usp=sharing
<https://urldefense.com/v3/__https://drive.google.com/drive/folders/1Yhx1goYDsv0FZdk4OfyH9_yRazkRSwMQ?usp=sharing__;!!Mak6IKo!NNZn_b2z9W4LO4J581E-MjNPOiP3Ycu6Aq4Tl1QDCtM_LLuFYDaL6-RQ1C6nAeNfanFjXrRNIOIfwQNCC_e1hx2HrX2Chw$>.
The locomotion policy trained for 0.5 m/s
longitudinal speed walking. The policy trained with
rigid terrain (about 6 hours) and we put it on scm
deformable terrain for testing purpose, in both rigid
and deformable terrain the evaluation looks decent.
For us, there are two major things in our todo list:
1) implement some parallelzation methods during
training. current 6 hour training time is trained
with one single simulation agent interact with RSL-RL
algorithm, we try to see if it's possible to do
faster by training with multiple simulation agents.
2) Include quadrupeds with CRM terrain in both policy
training and evaluation. See the attched picture, we
have a simulation script includes quadrupeds on CRM
terrain, but we need to merge this into RL pipeline.
Let me know if you have any questions, I am available
for meeting if you want me explain more about the
pipeline and details.
Best,
Harry
On Monday, July 28, 2025 at 4:40:48 PM UTC-5
[email protected] wrote:
Thank you so much for your response.
We are starting to work in this area as well,
from a slightly different perspective. Harry, a
great student in the lab, will share his findings
in about a week, to steer you in the right
direction. Are you working with a quadruped or
something similar?
I am working on a small-sized bioinspired
quadruped robot, and I look forward to the
findings from your student.
Is it like (a) the entire terrain is made up of
rocks 2cm to 6 cm, or (b) you have some
deformable terrain that once in a while has some
rocks 2 cm to 6 cm scattered around? This is
important, since (a) will likely take more time
to run than (b).
I am interested in having the entire terrain
composed of movable rocks, which the limbs can
interact with, such as each terrain box filled
with rocks of a specific size (2cm to 6cm). I am
also open to suggestions on whether this setup
might be computationally intensive and any
alternatives, such as option B. The image below
is what I have in mind (generated in MuJoCo, but
is fixed to the ground)
Screenshot 2025-07-28 172945.png
In principle, this should be doable but we
haven’t tried it yet. The needed modeling
elements are in Chrono, it’s just a matter of
putting a model like this together and seeing how
it does. Easier said than done, but we might be
able to provide some feedback when you cross that
bridge.
Thank you. I will provide more details on my
proposed implementation soon.
In about one week we’ll share a way to install a
newer version of PyChrono, a beta version, that
has support for what’s called in Chrono “the CRM
terrain” model. Might be helpful for you. Needs
an NVIDIA GPU to run.
Looking forward to the new version. I have been
facing some SDL2 package errors with my current
version. I gave more details in a different thread.
On Thursday, July 24, 2025 at 3:53:14 PM UTC-4
Dan Negrut wrote:
Please see my comments in this font below.
Thank you,
Dan
---------------------------------------------
Bernard A. and Frances M. Weideman Professor
NVIDIA CUDA Fellow
Department of Mechanical Engineering
Department of Computer Science
University of Wisconsin - Madison
4150ME, 1513 University Avenue
Madison, WI 53706-1572
608 772 0914 <tel:(608)%20772-0914>
http://sbel.wisc.edu/ <http://sbel.wisc.edu/>
http://projectchrono.org/
<https://urldefense.com/v3/__http://projectchrono.org/__;!!Mak6IKo!NNZn_b2z9W4LO4J581E-MjNPOiP3Ycu6Aq4Tl1QDCtM_LLuFYDaL6-RQ1C6nAeNfanFjXrRNIOIfwQNCC_e1hx3RfFuX_A$>
---------------------------------------------
*From:*Nnamdi Chikere <[email protected]>
*Sent:* Thursday, July 24, 2025 10:31 AM
*To:* Dan Negrut <[email protected]>
*Cc:* ProjectChrono <[email protected]>
*Subject:* Re: [chrono] Help with Granular
Simulation on Windows using PyChrono
Hello Professor,
Thank you so much for your reply.
For the first stage, I am trying to optimize
the parameters for control of the robot’s
locomotion on complex environments including
sand and rocks using CPGs. The end goal is to
later use RL to implement adaptive control
with changing terrains.
We are starting to work in this area as well,
from a slightly different perspective. Harry,
a great student in the lab, will share his
findings in about a week, to steer you in the
right direction. Are you working with a
quadruped or something similar?
For the rock sizes, I was looking to
implement three rock sizes ranging between
2cm and 6cm filled in a box on which the
robot will operate.
Is it like (a) the entire terrain is made up
of rocks 2cm to 6 cm; or (b) you have some
deformable terrain that once in a while has
some rocks 2 cm to 6 cm scattered around?
This is important, since (a) will likely take
more time to run than (b).
I am also trying to implement a flexible limb
that will be attached to the motors and
deform when in contact with some force or the
ground, is it possible to implement this to a
specified link?
In principle, this should be doable but we
haven’t tried it yet. The needed modeling
elements are in Chrono, it’s just a matter of
putting a model like this together and seeing
how it does. Easier said than done, but we
might be able to provide some feedback when
you cross that bridge.
I am working with Python and windows, and
would appreciate some help as regards this
In about one week we’ll share a way to
install a newer version of PyChrono, a beta
version, that has support for what’s called
in Chrono “the CRM terrain” model. Might be
helpful for you. Needs an NVIDIA GPU to run.
Best regards,
Nnamdi
On Thu, Jul 24, 2025 at 2:10 AM Dan Negrut
<[email protected]> wrote:
If you describe what you need to do with
your robot, we might be able to steer you
in the right direction.
How big are the rocks you mentioned?
What sort of end-goal do you have? Design
of robot, control of robot, RL, optimal
design, etc.
Dan
---------------------------------------------
Bernard A. and Frances M. Weideman Professor
NVIDIA CUDA Fellow
Department of Mechanical Engineering
Department of Computer Science
University of Wisconsin - Madison
4150ME, 1513 University Avenue
<https://urldefense.com/v3/__https:/www.google.com/maps/search/1513*University*Avenue**A0D*0A*Madison,*WI*53706-1572?entry=gmail&source=g__;KysrJSUrKys!!Mak6IKo!Od8BEUkPpm5WoEens9KtQIg47iogD1i2m9LDYGHeEv2M8GiWuVl9N0Stk5HjyF-DIbSFX75lMxV4hhD5$>
Madison, WI 53706-1572
<https://urldefense.com/v3/__https:/www.google.com/maps/search/1513*University*Avenue**A0D*0A*Madison,*WI*53706-1572?entry=gmail&source=g__;KysrJSUrKys!!Mak6IKo!Od8BEUkPpm5WoEens9KtQIg47iogD1i2m9LDYGHeEv2M8GiWuVl9N0Stk5HjyF-DIbSFX75lMxV4hhD5$>
608 772 0914 <tel:(608)%20772-0914>
http://sbel.wisc.edu/ <http://sbel.wisc.edu/>
http://projectchrono.org/
<https://urldefense.com/v3/__http:/projectchrono.org/__;!!Mak6IKo!Od8BEUkPpm5WoEens9KtQIg47iogD1i2m9LDYGHeEv2M8GiWuVl9N0Stk5HjyF-DIbSFX75lM0FtMmwz$>
---------------------------------------------
*From:*'Nnamdi Chikere' via ProjectChrono
<[email protected]>
*Sent:* Tuesday, July 22, 2025 6:16 PM
*To:* ProjectChrono
<[email protected]>
*Subject:* [chrono] Help with Granular
Simulation on Windows using PyChrono
Hello.
I am working on a simulation where a
robot moves on different terrains, and I
want to simulate the robot moving on sand
and rocks. I have been able to implement
the soil terrain using the SCM parameters
in PyChrono. Is it possible to implement
granular media, such as rocks, in
PyChrono? I attempted to install the DEME
package, but encountered several errors.
I am working with PyChrono 9.0 and Python
3.9.
Thank you.
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