Traditional supervised learning mostly works on individual tasks and requires
training on a large set of task-specific examples. This paradigm seriously
hinders the development of task generalization since preparing a task-specific
example set is costly. To build a system that can quickly and easily generalize
to new tasks, task instructions have been adopted as an emerging trend of
supervision recently. These instructions give the model the definition of the
task and allow the model to output the appropriate answer based on the
instructions and inputs. However, task instructions are often expressed in
different forms, which can be interpreted from two threads: first, some
instructions are short sentences and are pretrained language model (PLM)
oriented, such as prompts, while other instructions are paragraphs and are
human-oriented, such as those in Amazon MTurk; second, different end-users very
likely explain the same task with instructions of different textual
expressions. A robust system for task generalization should be able to handle
any new tasks regardless of the variability of instructions.
However, the system robustness in dealing with instruction-driven task
generalization is still unexplored. This work investigates the system
robustness when the instructions of new tasks are (i) maliciously manipulated,
(ii) paraphrased, or (iii) from different levels of conciseness. To our
knowledge, this is the first work that systematically studies how robust a PLM
is when it is supervised by instructions with different factors of variability.

本研究探讨了新任务指令被恶意篡改、改写或来自不同书面表达程度时，基于预训练语言模型的监督系统的稳健性。

从任务说明中学习的鲁棒性

Robustness of Learning from Task Instructions

Dynamically changing environments, unreliable state estimation, and operation
under severe resource constraints are fundamental challenges that limit the
deployment of small autonomous drones. We address these challenges in the
context of autonomous, vision-based drone racing in dynamic environments. A
racing drone must traverse a track with possibly moving gates at high speed. We
enable this functionality by combining the performance of a state-of-the-art
planning and control system with the perceptual awareness of a convolutional
neural network (CNN). The resulting modular system is both platform- and
domain-independent: it is trained in simulation and deployed on a physical
quadrotor without any fine-tuning. The abundance of simulated data, generated
via domain randomization, makes our system robust to changes of illumination
and gate appearance. To the best of our knowledge, our approach is the first to
demonstrate zero-shot sim-to-real transfer on the task of agile drone flight.
We extensively test the precision and robustness of our system, both in
simulation and on a physical platform, and show significant improvements over
the state of the art.

我们将最先进的规划和控制系统与卷积神经网络相结合，实现了自主机器人在动态环境中的高速飞行和对感知目标的实时认识，无需进行任何调整，同时通过领域随机化生成大量的模拟数据，使我们的系统对光照和目标出现变化具有鲁棒性，实现了在敏捷无人机飞行任务中的零次模拟到实际的成功展示。