Steve-Eye: Equiping LLM-based Embodied Agents with Visual Perception in Open Worlds

[Website] [Arxiv Paper]

Overview

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:

• 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.

• 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.

• 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).

Model

To be released soon

Dataset

To be released soon

Environmental Visual Captioning (ENV-VC) Results

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

Foundational Knowledge Question Answering (FK-QA) Results

	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%

Skill Planning Results

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

Citation

@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}
}