LeCAR-Lab / CoVO-MPC

Official implementation for the paper "CoVO-MPC: Theoretical Analysis of Sampling-based MPC and Optimal Covariance Design" accepted by L4DC 2024. CoVO-MPC is an optimal sampling-based MPC algorithm.

Home Page:https://lecar-lab.github.io/CoVO-MPC/

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πŸ› No training results for dual drone env

bzx20 opened this issue Β· comments

commented

❓ Issue

When I train the model, I receive no response using GPU. So I set checkpoints to run the code, only to find that it exits after an execution of function reset_env. When using CPU, I will wait for a long time before it begins computing. But I still cannot get the code running to end.

I use pid policy to check my environment, and the output seems normal and records the animation like this:

anim

The intermediate variable output for solving the equation is also normal.

πŸ€” Possible solutions

  1. Use logging to find which step goes wrong. (I have already used jax.debug.print(), but I received no logging results while training by GPU.)

  2. Check input and output of step function to see if there exits any invalid values.

commented

Test: Use only taut states, and apply thrust to balance gravity, set tau as zero.

Output

taut_action: [array(0.56196034, dtype=float32), array(0., dtype=float32), array(0.56196034, dtype=float32), array(0., dtype=float32)], no problem

Animation

It should hover, but
anim

TODO

Check taut_dual.py

Current issue

State Definition Optimizations: The current practice uses the positions of two quadrotors to define the state. This method, however, results in the introduction of unnecessary and redundant parameters. We propose to refine this approach by using the object's position instead of the quadrotor's position.

Misinterpretation of Angles: The angles $\theta, \phi$ are currently not represented correctly. These should be characterized in the world frame for a more accurate depiction.

image

Test results

anim

anim

Training results

anim
plot
ppo