Adaptive robot training by programming and guiding

The simplest and most common robot training method is operator-guiding, but this method is inflexible and limited to fixed sequences. Explicit textual robot programming has enjoyed many advances in the last decade and current research implementations are powerful. But explicit programming is complex and programmers must be specially trained. Mixed systems attempt to unite the facility of guiding with the power of programming. However, existing mixed systems exhibit mismatches in the interaction between programming and guiding, because they ignore underlying dependencies between the programmer and operator. An operator may (1) be given insufficient visual cues; (2) lack the required dexterity; or (3) be required to add unforeseen movement sequences to the program. This paper presents the design and implementation of ART (adaptive robot trainer), a prototypical mixed robot training system that eliminates or corrects deficiencies in visual cues and dexterity, and additionally improves the guiding and programming components. Mixed systems and assembly tasks are analysed, to give an effective representation of task state, which in turn motivates the design of ART's language to automate much of the program-guiding interaction. The language allows the programmer to express assembly operations and object-feature relationships in a natural way while providing the system with the information necessary to maintain the task state. The representation also enables the correction of guiding errors, flexibility in the guiding protocol and the generation of meaningful messages to prompt operator actions. These principles in the design and implementation pave the road to more instructable, capable robots.Now at Bell Northern Research, Ottawa.