BiGym：一個以演示驅動的行動雙手操作基準測試

BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark

July 10, 2024

作者: Nikita Chernyadev, Nicholas Backshall, Xiao Ma, Yunfan Lu, Younggyo Seo, Stephen James

cs.AI

摘要

我們介紹了 BiGym，一個針對行動雙手示範驅動機器人操作的新基準和學習環境。BiGym 包含40個不同任務，設定在家庭環境中，從簡單的目標達成到複雜的廚房清潔任務不等。為了準確捕捉現實世界的表現，我們為每個任務提供了人類收集的示範，反映了現實世界機器人軌跡中所發現的多樣性模態。BiGym 支持各種觀測，包括本體感覺數據和視覺輸入，例如 RGB 和來自3個攝影機視角的深度。為了驗證 BiGym 的可用性，我們在環境中全面評估了最先進的模仿學習算法和示範驅動強化學習算法，並討論未來的機遇。

English

We introduce BiGym, a new benchmark and learning environment for mobile bi-manual demo-driven robotic manipulation. BiGym features 40 diverse tasks set in home environments, ranging from simple target reaching to complex kitchen cleaning. To capture the real-world performance accurately, we provide human-collected demonstrations for each task, reflecting the diverse modalities found in real-world robot trajectories. BiGym supports a variety of observations, including proprioceptive data and visual inputs such as RGB, and depth from 3 camera views. To validate the usability of BiGym, we thoroughly benchmark the state-of-the-art imitation learning algorithms and demo-driven reinforcement learning algorithms within the environment and discuss the future opportunities.