Multimodal Behavior Tree Generation: A Small Vision-Language Model for Robot Task Planning
cs.RO
Submitted: 2026-03-06
Updated: 2026-09-16
Code: https://github.com/AIRLab-POLIMI/multimodal-BT
Terminology
Sources
- VLM-driven Behavior Tree for Context-aware Task Planning
- Video-to-BT: Generating Reactive Behavior Trees from Human Demonstration Videos for Robotic Assembly
- BTGenBot-2: Efficient Behavior Tree Generation with Small Language Models
- Integrating Intent Understanding and Optimal Behavior Planning for Behavior Tree Generation from Human Instructions
- Active Learning for Convolutional Neural Networks: A Core-Set Approach
- SmolVLM: Redefining small and efficient multimodal models
- Qwen2.5-VL Technical Report
- Gemma 3 Technical Report
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
- Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving