Related Experiment Video
Updated: Jan 7, 2026
11:01
SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
Published on: November 24, 2015
13.6K
A Survey of Behavior Foundation Model: Next-Generation Whole-Body Control System of Humanoid Robots
IEEE Transactions on Pattern Analysis and Machine Intelligence
|December 30, 2025
Summary
Behavioral foundation models (BFMs) offer a new approach to humanoid whole-body control (WBC). These models enable rapid adaptation to new tasks, overcoming limitations of traditional learning-based controllers for versatile humanoid robots.
Area of Science:
- Robotics
- Artificial Intelligence
- Control Systems
Background:
- Humanoid robots require sophisticated whole-body control (WBC) for complex tasks.
- Current learning-based controllers are limited by the need for extensive retraining for new scenarios.
- Behavioral foundation models (BFMs) present a novel paradigm for adaptable robot control.
Purpose of the Study:
- To provide a comprehensive overview of BFMs for humanoid WBC.
- To explore the development, applications, and challenges of BFMs in robotics.
- To highlight BFMs as a path towards scalable and general-purpose humanoid intelligence.
Main Methods:
- Reviewing diverse pre-training pipelines for BFMs.
- Analyzing real-world applications and limitations of current BFM approaches.
- Identifying urgent challenges and future research opportunities in BFM development for humanoids.
Main Results:
- BFMs leverage large-scale pre-training to learn reusable skills and behavioral priors.
- This enables zero-shot or rapid adaptation to a wide range of downstream tasks for humanoids.
- BFMs address the limitations of traditional controllers, offering more efficient and versatile control.
Conclusions:
- BFMs represent a significant advancement in achieving efficient and adaptable whole-body control for humanoid robots.
- They offer a promising direction for developing general-purpose physical intelligence in humanoids.
- Further research into BFMs is crucial for unlocking their full potential in real-world applications.
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