The pursuit of long-term autonomy mandates that robotic agents must
continuously adapt to their changing environments and learn to solve new tasks.
Continual learning seeks to overcome the challenge of catastrophic forgetting,
where learning to solve new tasks causes a model to forget previously learnt
information. Prior-based continual learning methods are appealing for robotic
applications as they are space efficient and typically do not increase in
computational complexity as the number of tasks grows. Despite these desirable
properties, prior-based approaches typically fail on important benchmarks and
consequently are limited in their potential applications compared to their
memory-based counterparts. We introduce Bayesian adaptive moment regularization
(BAdam), a novel prior-based method that better constrains parameter growth,
leading to lower catastrophic forgetting. Our method boasts a range of
desirable properties for robotic applications such as being lightweight and
task label-free, converging quickly, and offering calibrated uncertainty that
is important for safe real-world deployment. Results show that BAdam achieves
state-of-the-art performance for prior-based methods on challenging
single-headed class-incremental experiments such as Split MNIST and Split
FashionMNIST, and does so without relying on task labels or discrete task
boundaries.

通过引入贝叶斯自适应瞬间正则化（BAdam），我们提出了一种新的基于先验的方法，它更好地约束参数增长，从而降低了灾难性遗忘，并达到了基于先验方法在具有挑战性的单头类增量实验上的最新性能，如 Split MNIST 和 Split FashionMNIST，而不依赖任务标签或离散任务边界。

基于贝叶斯自适应矩阵正则化的鲁棒持续学习

Towards Robust Continual Learning with Bayesian Adaptive Moment  Regularization

The field of autonomous driving has grown tremendously over the past few
years, along with the rapid progress in sensor technology. One of the major
purposes of using sensors is to provide environment perception for vehicle
understanding, learning and reasoning, and ultimately interacting with the
environment. In this paper, we first introduce a multisensor platform allowing
vehicle to perceive its surroundings and locate itself in a more efficient and
accurate way. The platform integrates eleven heterogeneous sensors including
various cameras and lidars, a radar, an IMU (Inertial Measurement Unit), and a
GPS-RTK (Global Positioning System / Real-Time Kinematic), while exploits a ROS
(Robot Operating System) based software to process the sensory data. Then, we
present a new dataset (this https URL) for
autonomous driving captured many new research challenges (e.g. highly dynamic
environment), and especially for long-term autonomy (e.g. creating and
maintaining maps), collected with our instrumented vehicle, publicly available
to the community.

这篇研究论文介绍了一种多传感器平台，可以更有效和准确地感知车辆周围的环境，收集了一组新的自动驾驶数据集，并公开给社区研究人员使用。

自动驾驶多传感器欧盟长期数据集

EU Long-term Dataset with Multiple Sensors for Autonomous Driving

Autonomous systems will play an essential role in many applications across
diverse domains including space, marine, air, field, road, and service
robotics. They will assist us in our daily routines and perform dangerous,
dirty and dull tasks. However, enabling robotic systems to perform autonomously
in complex, real-world scenarios over extended time periods (i.e. weeks,
months, or years) poses many challenges. Some of these have been investigated
by sub-disciplines of Artificial Intelligence (AI) including navigation &
mapping, perception, knowledge representation & reasoning, planning,
interaction, and learning. The different sub-disciplines have developed
techniques that, when re-integrated within an autonomous system, can enable
robots to operate effectively in complex, long-term scenarios. In this paper,
we survey and discuss AI techniques as 'enablers' for long-term robot autonomy,
current progress in integrating these techniques within long-running robotic
systems, and the future challenges and opportunities for AI in long-term
autonomy.

该论文介绍了在复杂的长期实际情境下，人工智能技术对于自主机器人实现长期自主的重要性，综述了各个子领域中研究的技术及其在长期机器人自主方面的应用，讨论了当前集成各项技术的进展、未来挑战和机遇。