ForceVLA2 introduces explicit force awareness and hybrid control to Vision-Language-Action models, enabling stable contact-rich manipulation.
March 17, 2026
Original Paper
ForceVLA2: Unleashing Hybrid Force-Position Control with Force Awareness for Contact-Rich Manipulation
arXiv · 2603.15169
The Takeaway
Standard VLAs often fail at physical tasks like pressing or assembling because they only predict positions; ForceVLA2 adds the 'missing link' of force regulation. This allows robots to perform delicate or high-resistance tasks (like wiping or assembling) with significantly higher reliability and safety.
From the abstract
Embodied intelligence for contact-rich manipulation has predominantly relied on position control, while explicit awareness and regulation of interaction forces remain under-explored, limiting stability, precision, and robustness in real-world tasks. We propose ForceVLA2, an end-to-end vision-language-action framework that equips robots with hybrid force-position control and explicit force awareness. ForceVLA2 introduces force-based prompts into the VLM expert to construct force-aware task concep