Trajectory augmentation serves as a means to mitigate distributional shift in
imitation learning. However, imitating trajectories that inadequately represent
the original expert data can result in undesirable behaviors, particularly in
safety-critical scenarios. We propose a trajectory augmentation method designed
to maintain similarity with expert trajectory data. To accomplish this, we
first cluster trajectories to identify minority yet safety-critical groups.
Then, we combine the trajectories within the same cluster through geometrical
transformation to create new trajectories. These trajectories are then added to
the training dataset, provided that they meet our specified safety-related
criteria. Our experiments exhibit that training an imitation learning model
using these augmented trajectories can significantly improve closed-loop
performance.

我们提出了一种轨迹增强方法，旨在通过几何变换将位于同一簇中的轨迹组合起来，与专家轨迹数据保持相似性，从而创建新的轨迹。这些增强的轨迹被加入训练数据集中，在满足指定的安全相关标准的前提下。我们的实验证明，使用这些增强轨迹来训练仿真学习模型可以显著提高闭环性能。

增加安全关键行驶场景，同时保持与专家轨迹的相似性

Augmenting Safety-Critical Driving Scenarios while Preserving Similarity  to Expert Trajectories

Tuning parameters in model predictive control (MPC) presents significant
challenges, particularly when there is a notable discrepancy between the
controller's predictions and the actual behavior of the closed-loop plant. This
mismatch may stem from factors like substantial model-plant differences,
limited prediction horizons that do not cover the entire time of interest, or
unforeseen system disturbances. Such mismatches can jeopardize both performance
and safety, including constraint satisfaction. Traditional methods address this
issue by modifying the finite horizon cost function to better reflect the
overall operational cost, learning parts of the prediction model from data, or
implementing robust MPC strategies, which might be either computationally
intensive or overly cautious. As an alternative, directly optimizing or
learning the controller parameters to enhance closed-loop performance has been
proposed. We apply Bayesian optimization for efficient learning of unknown
model parameters and parameterized constraint backoff terms, aiming to improve
closed-loop performance of battery fast charging. This approach establishes a
hierarchical control framework where Bayesian optimization directly fine-tunes
closed-loop behavior towards a global and long-term objective, while MPC
handles lower-level, short-term control tasks. For lithium-ion battery fast
charging, we show that the learning approach not only ensures safe operation
but also maximizes closed-loop performance. This includes maintaining the
battery's operation below its maximum terminal voltage and reducing charging
times, all achieved using a standard nominal MPC model with a short horizon and
notable initial model-plant mismatch.

通过贝叶斯优化方法，针对模型预测控制中的参数调整问题，提出一种直接优化学习控制器参数的方法，以提高闭环性能。该方法在锂离子电池快速充电系统中应用，实现了安全充电和最大化闭环性能的目标。

通过贝叶斯优化学习模型预测控制参数实现电池快速充电

Learning Model Predictive Control Parameters via Bayesian Optimization  for Battery Fast Charging

Learning predictive models from observations using deep neural networks
(DNNs) is a promising new approach to many real-world planning and control
problems. However, common DNNs are too unstructured for effective planning, and
current control methods typically rely on extensive sampling or local gradient
descent. In this paper, we propose a new framework for integrated model
learning and predictive control that is amenable to efficient optimization
algorithms. Specifically, we start with a ReLU neural model of the system
dynamics and, with minimal losses in prediction accuracy, we gradually sparsify
it by removing redundant neurons. This discrete sparsification process is
approximated as a continuous problem, enabling an end-to-end optimization of
both the model architecture and the weight parameters. The sparsified model is
subsequently used by a mixed-integer predictive controller, which represents
the neuron activations as binary variables and employs efficient
branch-and-bound algorithms. Our framework is applicable to a wide variety of
DNNs, from simple multilayer perceptrons to complex graph neural dynamics. It
can efficiently handle tasks involving complicated contact dynamics, such as
object pushing, compositional object sorting, and manipulation of deformable
objects. Numerical and hardware experiments show that, despite the aggressive
sparsification, our framework can deliver better closed-loop performance than
existing state-of-the-art methods.

使用深度神经网络从观测中学习预测模型是解决现实世界规划和控制问题的一种有前景的新方法。本文提出了一个新的集成模型学习和预测控制框架，通过逐渐稀疏化神经网络以有效优化算法进行端到端的优化，从而在保证预测准确性的基础上取得更好的闭环控制性能。