用大规模数据+扩散策略，让机器人学会复杂双手操作。
ALOHA Unleashed: A Simple Recipe for Robot Dexterity
- 在ALOHA 2平台收集海量数据，配合扩散策略模型
- 在5个真实任务和3个模拟任务中超越现有最佳表现
- 适合对机器人灵巧操作感兴趣的开发者与研究者
近期工作表明，通过模仿学习可实现端到端机器人策略的训练。本文探讨了模仿学习在挑战性灵巧操作任务中的极限。研究发现，在ALOHA 2平台上进行大规模数据采集，并结合如扩散策略（Diffusion Policies）等表达能力强的模型，可在涉及柔体物体和复杂接触动力学的双臂操作任务中取得显著成效。我们在5个真实世界任务和3个仿真任务上验证了该方法的有效性，性能优于当前最先进的基线模型。项目网站与视频详见 aloha-unleashed.github.io。
原文摘要 · Abstract (English)
Recent work has shown promising results for learning end-to-end robot policies using imitation learning. In this work we address the question of how far can we push imitation learning for challenging dexterous manipulation tasks. We show that a simple recipe of large scale data collection on the ALOHA 2 platform, combined with expressive models such as Diffusion Policies, can be effective in learning challenging bimanual manipulation tasks involving deformable objects and complex contact rich dynamics. We demonstrate our recipe on 5 challenging real-world and 3 simulated tasks and demonstrate improved performance over state-of-the-art baselines. The project website and videos can be found at aloha-unleashed.github.io.
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