BiGym 2.0：人形家居操作的学习与智能体开发策略基准

中文摘要 AI 辅助

人形家居操作要求手臂在身体平衡、迈步和改变姿势的同时执行动作。我们提出了BiGym 2.0，这是BiGym针对Unitree G1的改编版本，涵盖20个家居任务，使用统一全身控制器进行演示和评估。该套件为每个任务提供60个原生人类虚拟现实演示，具有同步多摄像头视图和全身执行记录。我们基准测试了视觉-语言-动作微调、模仿学习、演示驱动的强化学习以及冷启动编码智能体，并给出了在线强化学习的交互预算。在相同的机载视图、本体感觉和全身控制器下，视觉-语言-动作微调在九个任务的平均表现上最高，而智能体开发的程序在该平均值上优于所有演示驱动的强化学习基线，并在双臂抓取方面领先。跨工作区堆叠仍然开放，π0.5在取箱任务上保持较低，多物体运输对于模仿学习、演示驱动的强化学习和编码智能体来说都很困难。所有环境、人类演示和评估轨迹均在以下https URL开源。

英文摘要

Humanoid household manipulation requires the arms to act while the body balances, steps and changes posture. We present BiGym 2.0, an adaptation of BiGym for the Unitree G1 across 20 household tasks using a unified whole-body controller for demonstration and evaluation. The suite provides 60 native human virtual-reality demonstrations per task with synchronised multi-camera views and full-body execution records. We benchmark vision-language-action fine-tuning, imitation learning, demo-driven reinforcement learning, and cold-start coding agents given the interaction budget of online reinforcement learning. With the same onboard views, proprioception and whole-body controller for every method, vision-language-action fine-tuning has the highest nine-task mean, and agent-developed programs outperform every demo-driven reinforcement learning baseline on this mean and lead on bimanual reaching. Cross-workspace stacking remains open, $π_{0.5}$ stays low on pick-box, and multi-object transport is hard for imitation learning, demo-driven reinforcement learning and coding agents. All environments, human demonstrations, and evaluation traces are open-sourced at https://github.com/swirl-uk/BiGym2.

发表机构

• Imperial College London（帝国理工学院）

机构由 AI 辅助整理，请以论文原文为准。