Enterprise AI Analysis
Endowing GPT-4 with a Humanoid Body: Building the Bridge Between Off-the-Shelf VLMs and the Physical World
By Yingzhao Jian, Zhongan Wang, Yi Yang, Hehe Fan
Published: 28 October 2025
Executive Summary: BiBo - Bridging VLMs to Humanoid Control
The core challenge in deploying humanoid agents in open, dynamic environments lies in their struggle to handle flexible and diverse interactions without prohibitively expensive, large-scale dataset collection.
This paper introduces BiBo (Building humanoId agent By Off-the-shelf VLMs), an innovative framework that empowers off-the-shelf Vision-Language Models (VLMs), such as GPT-4, to directly control humanoid agents.
BiBo circumvents the need for extensive data collection by leveraging the VLM's powerful open-world generalization capabilities. It operates through two key components: an embodied instruction compiler that translates high-level user instructions into precise low-level primitive commands, and a diffusion-based motion executor that generates human-like motions while dynamically adapting to physical feedback.
The impact is significant: BiBo achieves an impressive 90.2% interaction task success rate in complex open environments and enhances the precision of text-guided motion execution by 16.3% compared to prior methodologies, paving the way for more versatile and adaptable humanoid agents in the physical world.
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Interaction Task Success Rate
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Precision Improvement in Text-Guided Motion Execution
Deep Analysis & Enterprise Applications
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The BiBo Framework: VLM to Physical World
BiBo’s architecture seamlessly integrates advanced VLM reasoning with low-level physical execution, inspired by a computer's compiler-assembler model.
Enterprise Process Flow
High-level User Instruction
→
Embodied Instruction Compiler (VLM-driven)
→
Structured Command (Low-level primitives)
→
Diffusion-based Motion Executor
→
Generated Humanoid Motion (Physical World)
Significant Task Completion
BiBo demonstrates remarkable success in diverse interaction tasks within open environments, leveraging VLM generalization.
90.2%
Average Interaction Task Success Rate in Open Environments
Comparative Task Success Rates (Table 1)
BiBo significantly outperforms prior methods across a range of single and composite interaction tasks, even with online planning.
| Method (%) | Reach ↑ | Watch ↑ | Sit ↑ | Sleep ↑ | Touch ↑ | Lift ↑ | Simple ↑ | Medium ↑ | Hard ↑ |
|---|---|---|---|---|---|---|---|---|---|
| UniHSI(Xiao et al., 2023) | 93.28 | 81.03 | 85.11 | 69.62 | 44.90 | ||||
| HumanVLA (Xu et al., 2024) | 56.58 | 44.90 | |||||||
| TokenHSI (Pan et al., 2025) | 94.55 | 72.95 | 33.33 | 48.19 | |||||
| CLoSD (Tevet et al., 2024) | 85.83 | 87.76 | 76.99 | 34.67 | 42.55 | 7.71 | 26.47 | 7.05 | 2.38 |
| BiBo (ours) | 99.18 | 99.62 | 95.84 | 94.89 | 86.05 | 65.42 | 58.82 | 36.54 | 27.78 |
| BiBo (ours, GT plan) | 98.91 | 99.06 | 96.75 | 93.33 | 87.23 | 70.41 | 61.76 | 44.23 | 42.86 |
Adaptive Physical Interaction with Environmental Feedback
BiBo demonstrates superior adaptability and natural motion generation by integrating environmental feedback, handling complex scenarios like object collisions and maintaining motion continuity.
Seamless Collision Handling & Motion Continuity
Unlike methods that ignore collisions or introduce jitter, BiBo's diffusion-based motion executor, combined with its VAE design, ensures smooth transitions and adapts to physical feedback. For instance, in a hand-desk collision scenario (Figure 6), BiBo gradually redirects the hand upon surface contact, preserving motion continuity and preventing agent imbalance.
Natural & Precise Interactions
Visual comparisons (Figure 5) highlight BiBo's ability to generate more natural standing and leaning motions compared to less natural movements from UniHSI. Furthermore, BiBo achieves higher control precision in tasks like striking, raising hands, and grabbing objects, outperforming CLoSD and other baseline methods.
Enhanced Motion Execution Precision
BiBo's novel diffusion-based motion executor significantly improves the accuracy of text-guided motion execution.
16.3%
Precision Improvement Over Prior Methods
Calculate Your Potential AI ROI
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Projected Annual Impact
Estimated Annual Savings
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Productive Hours Reclaimed Annually
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Your AI Implementation Roadmap
A phased approach to integrate VLM-driven humanoid agents into your operations, from pilot to full-scale deployment.
Phase 01: Discovery & Pilot Program
Initial assessment of your current automation landscape and identification of key use cases for BiBo-like humanoid agents. Includes proof-of-concept development and pilot deployment in a controlled environment to validate core functionalities and gather initial performance metrics.
Phase 02: Customization & Integration
Tailoring BiBo's instruction compiler and motion executor to your specific operational workflows and existing IT infrastructure. This phase focuses on customizing environmental perception, refining task translation, and ensuring seamless integration with enterprise systems, potentially involving custom VLM fine-tuning or specialized motion datasets.
Phase 03: Scaled Deployment & Optimization
Expanding BiBo's deployment across multiple operational areas, leveraging its open-world generalization capabilities. Continuous monitoring, performance optimization, and iterative refinement based on real-world feedback to maximize efficiency, enhance adaptability, and ensure long-term stability in diverse physical environments.
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