Abstract
How can we build generalist robot systems? Scale may not be enough due to the significant multimodality of robotics tasks, lack of easily accessible data and the challenges of deploying on physical hardware. Meanwhile, most deployed robotic systems today are inherently modular and can leverage the independent generalization capabilities of each module to perform well. Therefore, this thesis seeks to tackle the task of building generalist robot agents by integrating these components into one: combining modularity with large-scale learning for general purpose robot control. The first question we consider is: how can we build modularity and hierarchy into learning systems? Our key insight is that rather than having the agent learn hierarchy and low-level control end-to-end, we can enforce modularity via planning to enable more efficient and capable robot learners. Next, we come to the role of scale in building generalist robot systems. To scale, neural networks require vast amounts of diverse data, expressive architectures to fit the data and a source of supervision to generate the data. We leverage a powerful supervision source: classical planning, which can generalize, but is expensive to run and requires access to privileged information to perform well in practice. We use these planners to supervise large-scale policy learning in simulation to produce generalist agents. Finally, we consider how to unify modularity with large-scale policy learning to build real-world robot systems capable of performing zero-shot manipulation. We do so by tightly integrating key ingredients of modular high and mid-level planning, learned local control, procedural scene generation and large-scale policy learning for sim2real transfer. We demonstrate that this recipe can produce a single, generalist agent that can solve challenging long-horizon manipulation tasks in the real world.
Abstract (translated)
如何构建通用机器人系统？由于机器人任务的多模态特性、缺乏易于获取的数据以及在物理硬件上部署所面临的挑战，单纯依赖规模可能并不足够。然而，当今大多数已部署的机器人系统本质上是模块化的，并能够利用每个模块独立泛化的能力来实现高性能。因此，本文旨在通过将这些组件整合为一个整体来解决构建通用型机器人代理的问题：结合模块化与大规模学习以用于通用目的机器人的控制。 首先，我们考虑的是如何在学习系统中引入模块化和层次结构？我们的关键见解是，与其让代理人从头到尾（端到端）学会层次结构和低级控制，不如通过规划来强制执行模块化，从而实现更高效且更具能力的机器人学习者。接下来，我们要讨论规模在构建通用型机器人系统中的作用。为了达到大规模，神经网络需要大量的多样化数据、能够适应这些数据的表达性架构以及生成数据的监督源。我们利用了一个强大的监督来源：经典规划，它可以泛化，但在实际运行中代价高昂，并且需要访问特权信息才能表现良好。我们使用这些规划器在模拟环境中监督大规模策略学习，以产生通用型代理。 最后，我们要考虑如何将模块化与大规模策略学习统一起来，以构建能够在零样本操作中执行的现实世界机器人系统。为此，我们将关键成分紧密结合：模块化的高级和中级计划、学习本地控制、程序化场景生成以及用于模拟到真实环境转换的大规模策略学习。我们证明了这一配方可以产生一个单一的、通用型代理，能够解决实际环境中具有挑战性的长时间操作任务。 总的来说，本文通过结合模块化设计与大规模学习的方法来探索构建能够在多种情况下执行复杂任务的一般性机器人系统的新途径。
URL
https://arxiv.org/abs/2503.06814
