Steve-Eye is an end-to-end trained large multimodal model to address this limitation, which integrates the LLM with a visual encoder to process visual-text inputs and generate multimodal feedback. We adopt a semi-automatic strategy to collect an extensive dataset comprising 850K open-world instruction pairs, enabling our model to encompass three essential functions for an agent: multimodal perception, foundational knowledge base, and skill prediction and planning. Our contribution can be summarized as:
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Open-World Instruction Dataset: We construct instruction data for the acquisition of three mentioned functions, which contains not only the agent’s per-step status and environmental features but also the essential knowledge for agents to act and plan.
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Large Multimodal Model and Training: Steve-Eye combines a visual encoder which converts visual inputs into a sequence of embeddings, along with a pre-trained LLM which empowers embodied agents to engage in skill or task reasoning in an open world.
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Open-World Benchmarks: We develop the following benchmarks to evaluate agent performance from a broad range of perspectives: (1) environmental visual captioning (ENV-VC); (2) foundational knowledge question answering (FK-QA); (3) skill prediction and planning (SPP).
To be released soon
To be released soon
| Model | Visual Encoder | Inventory  |
Equip  |
Object in Sight  |
Life  |
Food  |
Sky  |
|---|---|---|---|---|---|---|---|
| BLIP-2 | CLIP | 41.6 | 58.5 | 64.7 | 88.5 | 87.9 | 57.6 |
| Llama-2-7b | - | - | - | - | - | - | - |
| Steve-Eye-7b | VQ-GAN | 89.9 | 78.3 | 87.4 | 92.1 | 90.2 | 68.5 |
| Steve-Eye-13b | MineCLIP | 44.5 | 61.8 | 72.2 | 89.2 | 88.6 | 68.2 |
| Steve-Eye-13b | VQ-GAN | 91.1 | 79.6 | 89.8 | 92.7 | 90.8 | 72.7 |
| Steve-Eye-13b | CLIP | 92.5 | 82.8 | 92.1 | 93.1 | 91.5 | 73.8 |
| Scoring | Accuracy | |||||
|---|---|---|---|---|---|---|
| Wiki Page | Wiki Table | Recipe | TEXT All | TEXT | IMG | |
| --------------- | ------------ | --------- | -------- | ---------- | ---------- | ------- |
| Llama-2-7b | 6.90 | 6.21 | 7.10 | 6.62 | 37.01% | - |
| Llama-2-13b | 6.31 (-0.59) | 6.16 (-0.05) | 6.31 (-0.79) | 6.24 (-0.38) | 37.96% | - |
| Llama-2-70b | 6.91 (+0.01) | 6.97 (+0.76) | 7.23 (+0.13) | 7.04 (+0.42) | 38.27% | - |
| gpt-turbo-3.5 | 7.26 (+0.36) | 7.15 (+0.94) | 7.97 (+0.87) | 7.42 (+0.80) | 41.78% | - |
| Steve-Eye-7b | 7.21 (+0.31) | 7.28 (+1.07) | 7.82 (+0.72) | 7.54 (+0.92) | 43.25% | 62.83% |
| Steve-Eye-13b | 7.38 (+0.48) | 7.44 (+1.23) | 7.93 (+0.83) | 7.68 (+1.06) | 44.36% | 65.13% |
| Model |  |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MineAgent | 0.00 | 0.03 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.21 | 0.0 | 0.05 | 0.0 |
| gpt assistant | 0.30 | 0.17 | 0.07 | 0.00 | 0.03 | 0.00 | 0.20 | 0.00 | 0.20 | 0.03 | 0.13 | 0.00 | 0.10 | 0.00 |
| Steve-Eye-auto | 0.30 | 0.27 | 0.37 | 0.23 | 0.20 | 0.17 | 0.26 | 0.07 | 0.13 | 0.17 | 0.20 | 0.33 | 0.00 | 0.13 |
| Steve-Eye | 0.40 | 0.30 | 0.43 | 0.53 | 0.33 | 0.37 | 0.43 | 0.30 | 0.43 | 0.47 | 0.47 | 0.40 | 0.13 | 0.23 |
| Model |  |
 |
 |
 |
 |
 |
 |
 |
 |
 |
|---|---|---|---|---|---|---|---|---|---|---|
| MineAgent | 0.46 | 0.50 | 0.33 | 0.35 | 0.0 | 0.0 | 0.06 | 0.0 | 0.0 | 0.0 |
| gpt assistant | 0.57 | 0.76 | 0.43 | 0.30 | 0.00 | 0.00 | 0.37 | 0.00 | 0.03 | 0.00 |
| Steve-Eye-auto | 0.70 | 0.63 | 0.40 | 0.30 | 0.17 | 0 | 0.37 | 0.03 | 0.07 | 0.00 |
| Steve-Eye | 0.73 | 0.67 | 0.47 | 0.33 | 0.23 | 0.07 | 0.43 | 0.10 | 0.17 | 0.07 |
@article{zheng2023steve,
title={Steve-Eye: Equipping LLM-based Embodied Agents with Visual Perception in Open Worlds},
author={Zheng, Sipeng and Liu, Jiazheng and Feng, Yicheng and Lu, Zongqing},
journal={arXiv preprint arXiv:2310.13255},
year={2023}
}