開放世界中對關節物體的適應性移動操作

Adaptive Mobile Manipulation for Articulated Objects In the Open World

January 25, 2024

作者: Haoyu Xiong, Russell Mendonca, Kenneth Shaw, Deepak Pathak

cs.AI

摘要

在家庭等開放式非結構環境中部署機器人一直是一個長期存在的研究問題。然而，機器人通常只在封閉的實驗室環境中進行研究，以前的移動操作工作僅限於拾取-移動-放置，這在這個領域中僅僅是冰山一角。在本文中，我們介紹了開放世界移動操作系統，這是一種全棧方法，用於應對現實中的關節對象操作，例如現實世界中的門、櫥櫃、抽屜和冰箱等開放式非結構環境。機器人利用適應性學習框架，從一小組數據中通過行為克隆進行初始學習，然後通過對超出訓練分佈範圍的新對象進行在線實踐學習。我們還開發了一種低成本的移動操作硬件平台，能夠在非結構環境中進行安全和自主的在線適應，成本約為20,000美元。在我們的實驗中，我們在CMU校園的4棟建築中使用了20個可操作的對象。對於每個對象，僅需不到一小時的在線學習，系統就能將成功率從BC預訓練的50%提高到使用在線適應的95%。視頻結果請參見https://open-world-mobilemanip.github.io/

English

Deploying robots in open-ended unstructured environments such as homes has been a long-standing research problem. However, robots are often studied only in closed-off lab settings, and prior mobile manipulation work is restricted to pick-move-place, which is arguably just the tip of the iceberg in this area. In this paper, we introduce Open-World Mobile Manipulation System, a full-stack approach to tackle realistic articulated object operation, e.g. real-world doors, cabinets, drawers, and refrigerators in open-ended unstructured environments. The robot utilizes an adaptive learning framework to initially learns from a small set of data through behavior cloning, followed by learning from online practice on novel objects that fall outside the training distribution. We also develop a low-cost mobile manipulation hardware platform capable of safe and autonomous online adaptation in unstructured environments with a cost of around 20,000 USD. In our experiments we utilize 20 articulate objects across 4 buildings in the CMU campus. With less than an hour of online learning for each object, the system is able to increase success rate from 50% of BC pre-training to 95% using online adaptation. Video results at https://open-world-mobilemanip.github.io/