BiGym：一个以演示为驱动的移动双手操作基准测试

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

我们介绍了BiGym，这是一个针对移动双手示范驱动机器人操作的新基准和学习环境。BiGym包含40个多样化的任务，设定在家庭环境中，从简单的目标达成到复杂的厨房清洁等各种任务。为了准确捕捉真实世界的表现，我们为每个任务提供了人类收集的示范，反映了真实世界机器人轨迹中的多样性模态。BiGym支持各种观测数据，包括本体感知数据和视觉输入，如RGB和来自3个摄像头视角的深度。为了验证BiGym的可用性，我们在环境中彻底基准测试了最先进的模仿学习算法和示范驱动强化学习算法，并讨论了未来的机会。

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.