VADER: Visual Affordance Detection and Error Recovery for Multi Robot Human Collaboration

Michael Ahn; Montserrat Gonzalez Arenas; Matthew Bennice; Noah Brown; Christine Chan; Byron David; Anthony Francis; Gavin Gonzalez; Rainer Hessmer; Tomas Jackson; Nikhil J Joshi; Daniel Lam; Tsang-Wei Edward Lee; Alex Luong; Sharath Maddineni; Harsh Patel; Jodilyn Peralta; Jornell Quiambao; Diego Reyes; Rosario M Jauregui Ruano; Dorsa Sadigh; Pannag Sanketi; Leila Takayama; Pavel Vodenski; Fei Xia

arXiv · 2405.16021

VADER: Visual Affordance Detection and Error Recovery for Multi Robot Human Collaboration

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Abstract

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/

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Michael Ahn, Montserrat Gonzalez Arenas, Matthew Bennice, Noah Brown, Christine Chan, Byron David, Anthony Francis, Gavin Gonzalez, Rainer Hessmer, Tomas Jackson, Nikhil J Joshi, Daniel Lam, Tsang-Wei Edward Lee, Alex Luong, Sharath Maddineni, Harsh Patel, Jodilyn Peralta, Jornell Quiambao, Diego Reyes, Rosario M Jauregui Ruano, Dorsa Sadigh, Pannag Sanketi, Leila Takayama, Pavel Vodenski, Fei Xia. 2024-05-30. VADER: Visual Affordance Detection and Error Recovery for Multi Robot Human Collaboration. https://arxiv.org/abs/2405.16021

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