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Trivial: MS-HAB to docs
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arth-shukla committed Dec 20, 2024
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11 changes: 7 additions & 4 deletions docs/generate_task_docs.py
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"mobile_manipulation": """# Mobile Manipulation Tasks
These are tasks where a mobile manipulator is used to manipulate objects. This cateogry primarily uses robots with mobile bases like Fetch or Stretch robots""",
These are tasks where a mobile manipulator is used to manipulate objects. This cateogry primarily uses robots with mobile bases like Fetch or Stretch robots.
For additional tasks, including scene-level mobile manipulation, please check out the [community contributions page](../community_contributions/index.md).
""",

"quadruped": """# Quadruped Tasks
These are tasks where a quadruped robot is used for locomotion and/or manipulation. This cateogry primarily uses robots with four legs like the ANYmal or Unitree go robots""",
These are tasks where a quadruped robot is used for locomotion and/or manipulation. This cateogry primarily uses robots with four legs like the ANYmal or Unitree go robots.""",

"control": """# Control Tasks
These are classic control tasks where the objective is to control a robot to reach a particular state, similar to the [DM Control suite](https://github.com/deepmind/dm_control) but with GPU parallelized simulation and rendering""",
These are classic control tasks where the objective is to control a robot to reach a particular state, similar to the [DM Control suite](https://github.com/deepmind/dm_control) but with GPU parallelized simulation and rendering.""",

"drawing": """# Drawing Tasks
These are tasks where the robot is controlled to draw a specific shape or pattern""",
These are tasks where the robot is controlled to draw a specific shape or pattern.""",
}
import urllib.request
import mani_skill.envs
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78 changes: 78 additions & 0 deletions docs/source/tasks/community_contributions/index.md
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<!-- THIS IS ALL GENERATED DOCUMENTATION. DO NOT MODIFY THIS FILE -->
[asset-badge]: https://img.shields.io/badge/download%20asset-yes-blue.svg
[dense-reward-badge]: https://img.shields.io/badge/dense%20reward-yes-green.svg
[sparse-reward-badge]: https://img.shields.io/badge/sparse%20reward-yes-green.svg
[no-dense-reward-badge]: https://img.shields.io/badge/dense%20reward-no-red.svg
[no-sparse-reward-badge]: https://img.shields.io/badge/sparse%20reward-no-red.svg
[demos-badge]: https://img.shields.io/badge/demos-yes-green.svg
# Community Contributed Projects

These are tasks and external libraries contributed by the community, built in ManiSkill.
The document here has both a high-level overview/list of each project in a table as well as detailed task cards with video demonstrations after.

## Task Table
Table of all tasks/environments in this category. Task column is the environment ID, Preview is a thumbnail pair of the first and last frames of an example success demonstration. Max steps is the task's default max episode steps, generally tuned for RL workflows.
<table class="table">
<thead>
<tr class="row-odd">
<th class="head"><p>Task</p></th>
<th class="head"><p>Preview</p></th>
<th class="head"><p>Dense Reward</p></th>
<th class="head"><p>Success/Fail Conditions</p></th>
<th class="head"><p>Demos</p></th>
<th class="head"><p>Max Episode Steps</p></th>
</tr>
</thead>
<tbody>
<tr class="row-odd">
<td><p><a href="https://arth-shukla.github.io/mshab/" target="_blank">ManiSkill-HAB</a></p></td>
<td><div style='display:flex;gap:4px;align-items:center'><img style='min-width:min(50%, 200px);max-width:200px;height:auto' src='../../_static/env_thumbnails/ManiSkill-HAB_rt_thumb.png' alt='ManiSkill-HAB'></td>
<td><p>✅</p></td>
<td><p>✅</p></td>
<td><p>✅</p></td>
<td><p>Full Task: 1200-2000<br /><br />Subtasks: 200</p></td>
</tr>
</tbody>
</table>

## ManiSkill-HAB

**[Paper](https://arxiv.org/abs/2412.13211)** | **[Website](https://arth-shukla.github.io/mshab)** | **[Code](https://github.com/arth-shukla/mshab)** | **[Models](https://huggingface.co/arth-shukla/mshab_checkpoints)** | **[Dataset](https://arth-shukla.github.io/mshab/#dataset-section)**

![dense-reward][dense-reward-badge]
![sparse-reward][sparse-reward-badge]
![demos][demos-badge]
:::{dropdown} Task Card
:icon: note
:color: primary

**Task Description:**

ManiSkill-HAB contains 3 long-horizon tasks:
- **TidyHouse**: Move 5 target objects to different open receptacles (e.g. table, counter, etc).
- **PrepareGroceries**: Move 2 objects from the opened fridge to goal positions on the counter,
then 1 object from the counter to the fridge.
- **SetTable**: Move 1 bowl from the closed drawer to the dining table and 1 apple from the
closed fridge to the same dining table.

These tasks are split into 4 subtasks: Pick Place, Open, and Close.

**Randomizations:**
- Target object/articulation/goal (depending on subtask)
- Base x/y position and z rotation, arm and torso qpos noise

**Success Conditions:**
- Pick: Object grasped, end-effector at rest position, robot static
- Place: Object within 15cm of goal position, end-effector at rest position, robot static
- Open: Articulation open, end-effector at rest position, robot static
- Close: Articulation closed, end-effector at rest position, robot static

**Fail Conditions:**
- Robot exceeds cumulative collision force threshold (5000N for Pick, 7500N for Place, 10,000N for Open/Close)
:::

<div style="display: flex; justify-content: center;">
<video preload="none" controls="True" width="100%" style="max-width: min(100%, 512px);" poster="../../_static/env_thumbnails/ManiSkill-HAB_rt_poster.png">
<source src="https://github.com/haosulab/ManiSkill/raw/main/figures/environment_demos/ManiSkill-HAB_rt.mp4" type="video/mp4">
</video>
</div>
2 changes: 1 addition & 1 deletion docs/source/tasks/control/index.md
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[demos-badge]: https://img.shields.io/badge/demos-yes-green.svg
# Control Tasks

These are classic control tasks where the objective is to control a robot to reach a particular state, similar to the [DM Control suite](https://github.com/deepmind/dm_control) but with GPU parallelized simulation and rendering
These are classic control tasks where the objective is to control a robot to reach a particular state, similar to the [DM Control suite](https://github.com/deepmind/dm_control) but with GPU parallelized simulation and rendering.
The document here has both a high-level overview/list of all tasks in a table as well as detailed task cards with video demonstrations after.

## Task Table
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2 changes: 1 addition & 1 deletion docs/source/tasks/drawing/index.md
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[demos-badge]: https://img.shields.io/badge/demos-yes-green.svg
# Drawing Tasks

These are tasks where the robot is controlled to draw a specific shape or pattern
These are tasks where the robot is controlled to draw a specific shape or pattern.
The document here has both a high-level overview/list of all tasks in a table as well as detailed task cards with video demonstrations after.

## Task Table
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1 change: 1 addition & 0 deletions docs/source/tasks/index.md
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dextrous/index
digital_twins/index
drawing/index
community_contributions/index
```
5 changes: 4 additions & 1 deletion docs/source/tasks/mobile_manipulation/index.md
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[demos-badge]: https://img.shields.io/badge/demos-yes-green.svg
# Mobile Manipulation Tasks

These are tasks where a mobile manipulator is used to manipulate objects. This cateogry primarily uses robots with mobile bases like Fetch or Stretch robots
These are tasks where a mobile manipulator is used to manipulate objects. This cateogry primarily uses robots with mobile bases like Fetch or Stretch robots.

For additional tasks, including scene-level mobile manipulation, please check out the [community contributions page](../community_contributions/index.md).

The document here has both a high-level overview/list of all tasks in a table as well as detailed task cards with video demonstrations after.

## Task Table
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2 changes: 1 addition & 1 deletion docs/source/tasks/quadruped/index.md
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[demos-badge]: https://img.shields.io/badge/demos-yes-green.svg
# Quadruped Tasks

These are tasks where a quadruped robot is used for locomotion and/or manipulation. This cateogry primarily uses robots with four legs like the ANYmal or Unitree go robots
These are tasks where a quadruped robot is used for locomotion and/or manipulation. This cateogry primarily uses robots with four legs like the ANYmal or Unitree go robots.
The document here has both a high-level overview/list of all tasks in a table as well as detailed task cards with video demonstrations after.

## Task Table
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