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Published 20 hours ago • verified publisher icon clvrai

Questions Regarding the Repo

Open mihdalal opened this issue 2 years ago • 3 comments

Hi, I am very interested in using this environment for my research. I have the following questions:

  1. Is this repo still being developed/used? It seems the last commit was from last year so I just want to be sure everything is up to date.
  2. Is there a plan to switch to deepmind's mujoco backend (instead of mujoco py)
  3. Are there any plots with baseline results for RL/IL methods?
  4. Have any of the proposed follow ups from the paper been implemented? (mentioned at the end of paper such as realistic tool attachment or additional robot support).

mihdalal avatar Mar 31 '22 23:03 mihdalal

Glad to hear that you're interested in our environment.

  1. We have been working on this environment, but haven't completed it yet. For a pre-mature version, please refer to tstar branch.
  2. We don't have a plan to switch our MuJoCo backend. Is there any reason you want dm_control over mujoco_py?
  3. You can find learning curves of baseline methods in our ICRA paper: https://clvrai.com/assets/research/lee_icra21.pdf
  4. We have been mainly working on improving usability so far.

youngwoon avatar Apr 01 '22 06:04 youngwoon

  1. Can you describe what is the difference between master and tstar? If i was to start a new research project, I would ideally want something that isn't changing too much but also has working functionality with well tuned baselines.
  2. Mujoco_py hasn't been updated in years and will likely become deprecated at some point, especially given that DeepMind is now re-writing mujoco. Additionally, mujoco_py has many annoying setup and headless rendering issues that will never be fixed at this point. My suggestion was to use: https://github.com/deepmind/mujoco (not dm_control) since deepmind is actively improving it. I totally understand that this is a ton of work though so this was more to just bring it your attention.
  3. Thank you for the baseline curves! I would suggest adding this to the website, currently the website links to the old paper from 2019 which is heavily out of date it seems. :)
  4. Sounds good!

mihdalal avatar Apr 01 '22 19:04 mihdalal

Thank you for your detailed answers.

  1. The tstar branch includes improved dense reward functions and a scripted expert policy for demo collection.
  2. I don't have experience with DeepMind's MuJoCo python binding but it looks well maintained. I'll take a closer look and think about replacing mujoco_py with DeepMind's MuJoCo python binding. Thank you for your suggestion!
  3. Sure. Will update the website together with the future code update.

youngwoon avatar Apr 02 '22 16:04 youngwoon