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.

翻译：本文介绍BiGym，一个面向移动双臂机器人的基于演示的操作任务新基准与学习环境。BiGym包含40项在家庭环境中设计的多样化任务，涵盖从简单目标抓取到复杂厨房清洁等多种场景。为准确反映真实世界性能，我们为每项任务提供人工采集的演示数据，这些数据体现了真实机器人轨迹中存在的多模态特性。BiGym支持多种观测模式，包括本体感知数据以及来自3个相机视角的RGB视觉输入与深度信息。为验证BiGym的实用性，我们在该环境中系统评估了当前最先进的模仿学习算法与基于演示的强化学习算法，并探讨了未来研究方向。