  Robots today can exploit the rich world knowledge of large language models to
chain simple behavioral skills into long-horizon tasks. However, robots often
get interrupted during long-horizon tasks due to primitive skill failures and
dynamic environments. We propose VADER, a plan, execute, detect framework with
seeking help as a new skill that enables robots to recover and complete
long-horizon tasks with the help of humans or other robots. VADER leverages
visual question answering (VQA) modules to detect visual affordances and
recognize execution errors. It then generates prompts for a language model
planner (LMP) which decides when to seek help from another robot or human to
recover from errors in long-horizon task execution. We show the effectiveness
of VADER with two long-horizon robotic tasks. Our pilot study showed that VADER
is capable of performing complex long-horizon tasks by asking for help from
another robot to clear a table. Our user study showed that VADER is capable of
performing complex long-horizon tasks by asking for help from a human to clear
a path. We gathered feedback from people (N=19) about the performance of the
VADER performance vs. a robot that did not ask for help.
https://google-vader.github.io/
