From b3eaa78842462d0ee26cd746a3f49403fd81e688 Mon Sep 17 00:00:00 2001 From: ManiCM Date: Mon, 3 Jun 2024 02:26:29 +0800 Subject: [PATCH] Update index.html --- index.html | 244 +---------------------------------------------------- 1 file changed, 1 insertion(+), 243 deletions(-) diff --git a/index.html b/index.html index 7db9813..6a8ea24 100644 --- a/index.html +++ b/index.html @@ -1,243 +1 @@ - - - - - - - ManiCM - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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ManiCM

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Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation

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- - Guanxing Lu1*    - Zifeng Gao2*    - Tianxing Chen3    - Wenxun Dai3   
- Ziwei Wang4   and   - Yansong Tang1† - - - 1Tsinghua Shenzhen International Graduate School, Tsinghua University
- 2Harbin Institute of Technology, Shenzhen
- 3Shanghai AI Laboratory - 4Carnegie Mellon University - -

*Equal Contributions, †Corresponding author

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Abstract

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- Diffusion models have been verified to be effective in generating complex distributions from natural images to motion trajectories. Recent diffusion-based methods show impressive performance in 3D robotic manipulation tasks, whereas they suffer from severe runtime inefficiency due to multiple denoising steps, especially with high-dimensional observations. To this end, we propose a real-time robotic manipulation model named ManiCM that imposes the consistency constraint to the diffusion process, so that the model can generate robot actions in only one-step inference. -

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Framework of ManiCM

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- Given a raw action sequence a0, we first perform a forward diffusion to introduce -noise over n + k steps. The resulting noisy sequence an+k is then fed into both the online network and the -teacher network to predict the clean action sequence. The target network uses the teacher network’s k-step -estimation results to predict the action sequence. To enforce self-consistency, a loss function is applied to ensure -that the outputs of the online network and the target network are consistent. -

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Results

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- Comparisons on Runtime -
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- We evaluate 100 episodes on 31 challenging tasks from Adroit and Metaworld across 3 random seeds and report the time consumption per step (s) with standard deviation. The second results are underlined and the best results are bold. ‘∗’ denotes the reproduced version. The performance of our ManiCM in one-step inference surpasses all state-of-the-art models, providing ample evidence for the effectiveness of consistency distillation. -
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- Comparisons on Success Rate -
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- We evaluate 100 episodes on 31 challenging tasks from Adroit and Metaworld across 3 random seeds and report the success rates (%) with standard deviation. The second results are underlined and the best results are bold. ‘∗’ denotes the reproduced version. The performance of our ManiCM in one-step inference surpasses all state-of-the-art models, providing ample evidence for the effectiveness of consistency distillation. -
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Acknowledgements

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- Incoming -

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BibTeX

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