SCALE: Causal Learning and Discovery of Robot Manipulation Skills using Simulation
SCALE: Causal Learning and Discovery of Robot Manipulation Skills using Simulation
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发表时间：
2023
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通讯作者：
T. Lee;Shivam Vats;Siddharth Girdhar;Oliver Kroemer
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作者：
T. Lee;Shivam Vats;Siddharth Girdhar;Oliver Kroemer
：我们提出了 SCALE，一种从有限的数据集中发现和学习多种可解释的机器人技能的方法。我们不是学习可能无法捕获数据中所有模式的单一技能，而是首先通过因果推理识别不同的模式，并为每个模式学习单独的技能。我们的主要见解是将每种模式与一组独特的因果相关上下文变量相关联，这些变量是通过在模拟中执行因果干预而发现的。这使得能够根据生成数据的因果过程进行数据分区，然后可以训练忽略不相关变量的压缩技能。我们将每个机器人技能建模为区域压缩选项，它通过将因果过程及其相关变量与选项相关联来扩展选项框架。建模为技能数据生成区域，每个因果过程本质上都是局部的，因此仅在上下文空间的子集上有效。我们展示了我们用于两个代表性操作任务的方法：不确定性下的块堆叠和孔中插入。我们的实验表明，我们的方法产生了多种技能，这些技能紧凑、对领域转换具有鲁棒性，并且适合模拟到真实的迁移。
: We propose SCALE, an approach for discovering and learning a diverse set of interpretable robot skills from a limited dataset. Rather than learning a single skill which may fail to capture all the modes in the data, we first identify the different modes via causal reasoning and learn a separate skill for each of them. Our main insight is to associate each mode with a unique set of causally relevant context variables that are discovered by performing causal interventions in simulation. This enables data partitioning based on the causal processes that generated the data, and then compressed skills that ignore the irrelevant variables can be trained. We model each robot skill as a Regional Compressed Option, which extends the options framework by associating a causal process and its relevant variables with the option. Modeled as the skill Data Generating Region, each causal process is local in nature and hence valid over only a subset of the context space. We demonstrate our approach for two representative manipulation tasks: block stacking and peg-in-hole insertion under uncertainty. Our experiments show that our approach yields diverse skills that are compact, robust to domain shifts, and suitable for sim-to-real transfer.