在混乱的环境中自动二次运动的敏捷飞行需要受到限制的运动计划和控制，但要受翻译和旋转动力学的影响。传统的基于模型的方法通常需要复杂的设计和重型计算。在本文中，我们开发了一种基于深厚的增强学习方法，该方法解决了通过动态狭窄大门飞行的挑战性任务。我们设计了一个模型预测控制器，其自适应跟踪参考参考由深神经网络（DNN）进行了参数。这些参考文献包括遍历时间和四型SE（3）遍历姿势，这些姿势鼓励机器人从各种初始条件中使用最大的安全边缘飞行大门。为了应对在高度动态环境中的训练困难，我们开发了一个增强的学习框架，以有效地训练DNN，从而很好地介绍了各种环境。此外，我们提出了一种二进制搜索算法，该算法允许在线适应（3）对动态门的引用。最后，通过广泛的高保真模拟，我们表明我们的方法对门的速度不确定性具有鲁棒性，并适应了不同的门轨迹和方向。

估计和对外部干扰的反应对于二次驾驶的稳健飞行控制至关重要。现有的估计器通常需要针对特定​​的飞行方案或具有大量现实世界数据的培训进行重大调整，以实现令人满意的性能。在本文中，我们提出了一个神经移动范围估计器（Neuromhe），该估计量可以自动调整由神经网络建模并适应不同飞行方案的MHE参数。我们通过将MHE估计值的分析梯度推导出相对于可调参数的分析梯度实现这一目标，从而使MHE无缝嵌入作为神经网络中的无缝嵌入以进行高效学习。最有趣的是，我们证明可以从递归形式的卡尔曼过滤器有效地解决梯度。此外，我们开发了一种基于模型的策略梯度算法，可以直接从轨迹跟踪误差中训练神经元，而无需进行基础真相干扰。通过在各种具有挑战性的飞行中对四摩特的模拟和物理实验，通过模拟和物理实验对神经元的有效性进行了广泛的验证。值得注意的是，NeuroMhe的表现优于最先进的估计器，仅使用2.5％的参数量，力估计误差降低了49.4％。所提出的方法是一般的，可以应用于其他机器人系统的稳健自适应控制。

对外部干扰的估计和反应对于对准轮运动器的鲁棒控制是根本的重要性。现有估计人通常需要大量数据，包括地面真理的大量数据，以实现令人满意的性能。本文提出了一种数据有效的可微分运动地平线估计（DMHE）算法，可以在线自动调整MHE参数，并适应不同的场景。我们通过从MHE相对于调谐参数导出估计的轨迹的分析梯度来实现这一点，使能够进行自动调整的端到端学习。最有趣的是，我们表明可以从递归形式的卡尔曼滤波器有效地计算梯度。此外，我们开发了一种基于模型的策略梯度算法，可以直接从轨迹跟踪误差中学习参数，而无需对实际真理。所提出的DMHE可以进一步嵌入为具有用于联合优化的其他神经网络的层。最后，我们通过在四轮官上的模拟和实验中展示了所提出的方法的有效性，其中检查了突然有效载荷变化和飞行中的具有挑战性的情景。

Vision-based tactile sensors have gained extensive attention in the robotics community. The sensors are highly expected to be capable of extracting contact information i.e. haptic information during in-hand manipulation. This nature of tactile sensors makes them a perfect match for haptic feedback applications. In this paper, we propose a contact force estimation method using the vision-based tactile sensor DIGIT, and apply it to a position-force teleoperation architecture for force feedback. The force estimation is done by building a depth map for DIGIT gel surface deformation measurement and applying a regression algorithm on estimated depth data and ground truth force data to get the depth-force relationship. The experiment is performed by constructing a grasping force feedback system with a haptic device as a leader robot and a parallel robot gripper as a follower robot, where the DIGIT sensor is attached to the tip of the robot gripper to estimate the contact force. The preliminary results show the capability of using the low-cost vision-based sensor for force feedback applications.

用户生成的内容（UGC）的盲图质量评估（BIQA）遭受范围效应，表明在整体质量范围，平均意见评分（MOS）和预测的MOS（PMO）（PMO）上有很好的相关性；关注特定范围，相关性较低。范围效应的原因是，在较大范围内和狭窄范围内的预测偏差破坏了MOS和PMO之间的均匀性。为了解决这个问题，提出了一种新的方法，从粗粒度度量到细粒度的预测。首先，我们为粗粒度度量设计了排名和梯度损失。损失保持了PMOS和MOS之间的顺序和毕业生一致性，从而在较大范围内减少了预测的偏差。其次，我们提出多级公差损失以进行细粒度的预测。损失受到减少阈值的限制，以限制较窄和较窄范围的预测偏差。最后，我们设计了一个反馈网络来进行粗到精细的评估。一方面，网络采用反馈块来处理多尺度的失真功能，另一方面，它将非本地上下文功能融合到每次迭代的输出中，以获取更多质量感知的功能表示。实验结果表明，与最先进的方法相比，提出的方法可以减轻范围效应。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.