Summary of Sam-e: Leveraging Visual Foundation Model with Sequence Imitation For Embodied Manipulation, by Junjie Zhang et al.
SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation
by Junjie Zhang, Chenjia Bai, Haoran He, Wenke Xia, Zhigang Wang, Bin Zhao, Xiu Li, Xuelong Li
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes SAM-E, a novel architecture for robot manipulation that leverages a vision-foundation model for scene understanding and sequence imitation for long-term action reasoning. The approach combines Segment Anything (SAM) pre-trained on images with fine-tuning on robot data to better understand embodied scenarios. To address long-horizon reasoning, the paper introduces a multi-channel heatmap that enables action sequence prediction in a single pass, enhancing execution efficiency. Experimental results from instruction-following tasks demonstrate SAM-E’s superior performance and improved generalization in few-shot adaptation to new tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve robot manipulation by creating a new way for robots to understand scenes and predict actions. The approach uses pre-trained models that learn from images and fine-tune them with data specific to robots. This allows the robot to better understand its environment and make more accurate predictions about what it needs to do. The results show that this approach works well and can even adapt quickly to new tasks. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Generalization » Sam » Scene understanding
