# Description

This PR provides remake and extension to orginal environment
kuka-allegro-reorientation implemented in paper:
DexPBT: Scaling up Dexterous Manipulation for Hand-Arm Systems with
Population Based Training
(https://arxiv.org/abs/2305.12127)
[Aleksei
Petrenko](https://arxiv.org/search/cs?searchtype=author&query=Petrenko,+A),
[Arthur
Allshire](https://arxiv.org/search/cs?searchtype=author&query=Allshire,+A),
[Gavriel
State](https://arxiv.org/search/cs?searchtype=author&query=State,+G),
[Ankur
Handa](https://arxiv.org/search/cs?searchtype=author&query=Handa,+A),
[Viktor
Makoviychuk](https://arxiv.org/search/cs?searchtype=author&query=Makoviychuk,+V)

and another environment kuka-allegro-lift implemented in paper:
Visuomotor Policies to Grasp Anything with Dexterous Hands
(https://dextrah-rgb.github.io/)
[Ritvik Singh](https://www.ritvik-singh.com/), [Arthur
Allshire](https://allshire.org/), [Ankur
Handa](https://ankurhanda.github.io/), [Nathan
Ratliff](https://www.nathanratliff.com/), [Karl Van
Wyk](https://scholar.google.com/citations?user=TCYAoF8AAAAJ&hl=en)


Though this is a remake, this remake ends up differs quite a lot in
environment details for reasons like:
1. Simplify reward structure,
2. Unify environment implemtation,
3. Standarize mdp,
4. Utilizes manager-based API

That in my opinion, makes environment study and extension more
accessible, and analyzable. For example you can train lift policy first
then continuing the checkpoint in reorientation environment, since they
share the observation space. : ))

It is a best to consider this a very careful re-interpretation rather
than exact execution to migrate them to IsaacLab

Here is the training curve if you just train with
`./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task
Isaac-Dexsuite-Kuka-Allegro-Lift-v0 --num_envs 8192 --headless`

`./isaaclab.sh -p -m torch.distributed.run --nnodes=1 --nproc_per_node=4
scripts/reinforcement_learning/rsl_rl/train.py --task
Isaac-Dexsuite-Kuka-Allegro-Reorient-v0 --num_envs 40960 --headless
--distributed`

lift training ~ 4 hours
reorientation training ~ 2 days

Note that it requires a order of magnitude more data and time for
reorientation to converge compare to lift under almost identical setup

training curve(screen captured from Wandb) - reward, 
Cyan: reorient, Purple: Lift
<img width="1487" height="780" alt="Screenshot from 2025-09-07 22-58-13"
src="https://github.com/user-attachments/assets/bfa911de-4fee-4c0d-b39c-e9c33fae28f4"
/>


video results 
lift

![cone_lift](https://github.com/user-attachments/assets/e626eadb-b281-4ec9-af16-57f626fcc6aa)

![fat_capsule_lift](https://github.com/user-attachments/assets/cde57d4c-ceb2-40ab-88dd-44320da689c5)

reorient

![cube_reorient](https://github.com/user-attachments/assets/752809cb-ea19-4701-b124-20c1909e4566)

![rod_reorient](https://github.com/user-attachments/assets/f009605a-d93c-491c-b124-ff08606c63ec)


Memo:
I really enjoy working on this remake, and hopefully for whoever plan to
play and extend on this remake find it helpful and similarily joyful as
I did. I will be very excited to see what you got : ))

Octi


CAUTION:
Do Not Merge until the asset is uploaded to S3 bucket!

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- New feature (non-breaking change which adds functionality)

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