Task analysis

Mathematics

Machine learning

Mathematical analysis

Artificial intelligence

Management

Economics

Generalization

Programming language

Programming by demonstration

Inductive logic programming

Computer science

Task (project management)

Artificial neural network

Robot

Subroutine

In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program induction. NTP takes as input a task specification (e.g., video demonstration of a task) and recursively decomposes it into finer sub-task specifications. These specifications are fed to a hierarchical neural program, where bottom-level programs are callable subroutines that interact with the environment. We validate our method in three robot manipulation tasks. NTP achieves strong generalization across sequential tasks that exhibit hierarchal and compositional structures. The experimental results show that NTP learns to generalize well towards unseen tasks with increasing lengths, variable topologies, and changing objectives.stanfordvl.github.io/ntp/.

Neural Task Programming: Learning to Generalize Across Hierarchical Tasks