mit-acl / mppi_numba Goto Github PK

Going through the barebones notebook seems like the lamba is set to 1 throughout the whole run of MPPI. Any specific reasons to not update lambda?

Hello, and thank you for making this amazing work available on GitHub.

I have a question regarding to the clamping of control inputs in the #mppi.py and also #barebone_mppi_numba.ipynb notebook

I understand from the paper of MPPI usually they clamp the control sequence before it passes through the dynamics, which is in the mppi.py code file, it is inside the #rollout_numba function

v_noisy = max(vrange_d[0], min(vrange_d[1], v_nom))
w_noisy = max(wrange_d[0], min(wrange_d[1], w_nom))

My confusion is why there is another clamping function again after we weight the control sequence inside of the #update_useq_numba function?,

# Blocks crop the control together
tgap = int(math.ceil(timesteps / num_threads))
starti = min(tid*tgap, timesteps)
endi = min(starti+tgap, timesteps)
for ti in range(starti, endi):
u_cur_d[ti, 0] = max(vrange_d[0], min(vrange_d[1], u_cur_d[ti, 0]))
u_cur_d[ti, 1] = max(wrange_d[0], min(wrange_d[1], u_cur_d[ti, 1]))

Isn't it enough to just clamp it inside the rollout function?, I am looking forward to hearing from you Thank you

Hi there,

Thank you for sharing your amazing work!
I am trying to change your unicycle model to ackermann-based vehicle model.
Also, I want to deploy robot for the real-world test.

To do this, I am looking into your test code, and found "use_costmap" parameter.
After your first release, is there any progress for ROS costmap version?

Again, thank you very much!

Best,