ManiGaussian
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Tsinghua University,Nanyang Technological University,Carnegie Mellon University (CMU)
- Organisation type
- Academia,Academia,Academia
- Country
- China, Singapore, United States of America
- Published
- 13 March 2024
- Authors
- Guanxing Lu, Shiyi Zhang, Ziwei Wang, Changliu Liu, Jiwen Lu, Yansong Tang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Robotics, Vision, Video
- Task
- Robotic manipulation
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA GeForce RTX 4090
- Chips used
- 2
- Power draw
- 1.8 kW
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Open source
MIT license https://github.com/GuanxingLu/ManiGaussian
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
- Citations
- 129
1 Introduction: "We evaluate our ManiGaussian method on the RLBench dataset [27] with 10 tasks and 166 variants, where our method outperforms the state-of-the-art multi-task robotic manipulation methods by 13.1% in the average task success rate"
Sources
Where this record came from and when it was last checked.
- Reference
- ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
ManiGaussian was published by Tsinghua University,Nanyang Technological University,Carnegie Mellon University (CMU), in China, in March 2024. The organisation is categorised as academia,Academia,Academia.
It works in Robotics, Vision, Video, and is recorded as doing robotic manipulation.
The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.
How it was trained
The reason it appears in this catalogue at all is sOTA improvement.
Answers
ManiGaussian — common questions
Who created ManiGaussian?
ManiGaussian was published by Tsinghua University,Nanyang Technological University,Carnegie Mellon University (CMU), based in China, categorised as academia,Academia,Academia.
When was ManiGaussian released?
ManiGaussian was published in March 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is ManiGaussian used for?
ManiGaussian works in Robotics, Vision, Video, and is recorded as handling robotic manipulation. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
Where can I download ManiGaussian?
The weights for ManiGaussian are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
What GPU do I need to run ManiGaussian?
We cannot say. ManiGaussian has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Is ManiGaussian open source?
Its weights are published, so ManiGaussian can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
How many parameters does ManiGaussian have?
No parameter count has been published for ManiGaussian, which is why no memory or speed figure appears on this page.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.