VADER: マルチロボット・人間協調のための視覚的アフォーダンス検出とエラー回復
VADER: Visual Affordance Detection and Error Recovery for Multi Robot Human Collaboration
視覚的アフォーダンス検出とエラー認識を行い、他ロボットや人間への助け要請を新たなスキルとして組み込むことで、長期的タスクの中断から回復して完遂するフレームワークVADERを提案した。
著者: 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
分類: cs.RO
原文アブストラクト
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/
