Consider the following problem: given a few demonstrations of a task across a
few different objects, how can a robot learn to perform that same task on new,
previously unseen objects? This is challenging because the large variety of
objects within a class makes it difficult to infer the task-relevant
relationship between the new objects and the objects in the demonstrations. We
address this by formulating imitation learning as a conditional alignment
problem between graph representations of objects. Consequently, we show that
this conditioning allows for in-context learning, where a robot can perform a
task on a set of new objects immediately after the demonstrations, without any
prior knowledge about the object class or any further training. In our
experiments, we explore and validate our design choices, and we show that our
method is highly effective for few-shot learning of several real-world,
everyday tasks, whilst outperforming baselines. Videos are available on our
project webpage at this https URL

通过将模仿学习作为对象图表示之间条件对齐问题来解决机器人在新对象上学习任务的困难，使得机器人能够在示范之后立即在一组新对象上执行任务，无需先验知识或进一步训练。

通过隐式图对齐进行少样本上下文内模仿学习

Few-Shot In-Context Imitation Learning via Implicit Graph Alignment

We propose a novel algorithm for learning fair representations that can
simultaneously mitigate two notions of disparity among different demographic
subgroups in the classification setting. Two key components underpinning the
design of our algorithm are balanced error rate and conditional alignment of
representations. We show how these two components contribute to ensuring
accuracy parity and equalized false-positive and false-negative rates across
groups without impacting demographic parity. Furthermore, we also demonstrate
both in theory and on two real-world experiments that the proposed algorithm
leads to a better utility-fairness trade-off on balanced datasets compared with
existing algorithms on learning fair representations for classification.

本篇研究旨在提出一种新算法，用于学习公平的表征，以在分类环境下同时缓解不同人口统计特征子组之间的两种不平等概念。通过平衡误差率和条件对齐的表征等两个关键组件，它对确保跨组别的准确平等性和平衡假阳性和假阴性率发挥了作用。此外，我们还通过理论和两个实际实验证明，与现有的学习公平表征的算法相比，在平衡数据集上提出的算法可以在保持效用公平性的前提下得到更好的公平性。