The Therblig-Based Backbone Framework (TBBF) transforms how robots learn and execute complex, long-horizon tasks by breaking them into interpretable, reusable components.

• Enhanced Interpretability - Decomposes tasks into therblig-level units for clearer understanding
• Improved Generalization - Enables robots to adapt learned skills to new scenarios
• Data Efficiency - Leverages expert demonstrations to reduce training requirements
• Adaptive Trajectories - Generates appropriate motion paths for unfamiliar situations

This engineering breakthrough addresses fundamental limitations in end-to-end robot learning approaches, making industrial automation more reliable, flexible, and deployable across varied environments.

A Backbone for Long-Horizon Robot Task Understanding