多模式演示为机器人提供了大量信息，以使世界有意义。但是，当从人类示威中学习感觉运动控制政策时，这种丰度可能并不总是会导致良好的表现。无关的数据模式可能导致状态过度规格，在该状态中包含的模式不仅可以在决策中无用，而且可以改变跨环境的数据分布。州过度规格会导致诸如学习的政策之类的问题，而不是在培训数据分布之外推广。在这项工作中，我们提出了掩盖的模仿学习（MIL），以选择性地使用信息方式来解决状态过度指定。具体来说，我们设计了带有二进制掩码的蒙版策略网络，以阻止某些方式。我们开发了一种双层优化算法，该算法可以学习此面具以准确过滤过度指定的模态。我们从经验上证明，使用Robomimic数据集在包括Mujoco和机器人ARM环境在内的模拟域中的基线算法均优于基线算法，并有效地在收集在真实机器人上收集的多模式数据集中有效地恢复了环境不变的模式。我们的项目网站在以下网址介绍了我们的结果的补充详细信息和视频：https：//tinyurl.com/masked-il

现有的自动驾驶管道将感知模块与预测模块分开。这两个模块通过手工挑选的功能（例如代理框和轨迹）作为接口进行通信。由于这种分离，预测模块仅从感知模块接收部分信息。更糟糕的是，感知模块的错误会传播和积累，从而对预测结果产生不利影响。在这项工作中，我们提出了VIP3D，这是一种视觉轨迹预测管道，利用原始视频的丰富信息来预测场景中代理的未来轨迹。VIP3D在整个管道中采用稀疏的代理查询，使其完全可区分和可解释。此外，我们为这项新型的端到端视觉轨迹预测任务提出了评估度量。Nuscenes数据集的广泛实验结果表明，VIP3D在传统管道和以前的端到端模型上的强劲性能。

垂直联合学习（VFL）引起了很多关注，因为它可以以隐私的方式实现跨核数据合作。虽然大多数在VFL专注于线性和树模型的研究工作，但在VFL中尚未对深层模型（例如，神经网络）进行很好的研究。在本文中，我们专注于Splitnn，这是VFL中著名的神经网络框架，并确定了SplitNN中数据安全性和模型性能之间的权衡。简而言之，SplitNN通过交换梯度和转换数据来训练模型。一方面，SplitNN遭受了模型性能的损失，因为多方使用转换的数据而不是原始数据共同训练模型，并且丢弃了大量的低级特征信息。另一方面，通过在SplitNN中的较低层的汇总（即，数据的转换较小，保留了更低级别的功能）来提高模型性能的天真解决方案，使原始数据易受推理攻击的影响。为了减轻上述权衡，我们在VFL中提出了一个新的神经网络协议，称为安全远射聚合（SFA）。它改变了汇总转换数据并采用可移动掩码以保护原始数据的方式。实验结果表明，具有SFA的网络同时实现了数据安全性和高模型性能。

自动驾驶系统需要对周围环境有很好的了解，包括移动障碍物和静态高清（HD）语义图。现有方法通过离线手动注释来解决语义图问题，该注释遭受了严重的可伸缩性问题。最新的基于学习的方法产生了密集的栅格分割预测，这些预测不包含单个地图元素的实例信息，并且需要涉及许多手工设计的组件的启发式后处理，以获得矢量化的地图。为此，我们引入了一个端到端矢量化的高清图学习管道，称为ve​​ctormapnet。 Vectormapnet进行了板载传感器的观测值，并预测了鸟类视图中的一组稀疏的散布原料，以建模HD地图的几何形状。基于此管道，我们的方法可以明确地对地图元素之间的空间关系进行建模，并生成对矢量化的地图，这些矢量图对于下游自主驾驶任务友好而无需进行后处理。在我们的实验中，VectorMapnet在Nuscenes数据集上实现了强大的HD MAP学习性能，从而超过了先前的最新方法，可以通过14.2地图。从定性上讲，我们还表明Vectormapnet能够生成综合地图并捕获更多的道路几何细节。据我们所知，VectorMapnet是针对端到端矢量化的HD MAP学习问题设计的第一部作品。

The topic of multi-person pose estimation has been largely improved recently, especially with the development of convolutional neural network. However, there still exist a lot of challenging cases, such as occluded keypoints, invisible keypoints and complex background, which cannot be well addressed. In this paper, we present a novel network structure called Cascaded Pyramid Network (CPN) which targets to relieve the problem from these "hard" keypoints. More specifically, our algorithm includes two stages: Glob-alNet and RefineNet. GlobalNet is a feature pyramid network which can successfully localize the "simple" keypoints like eyes and hands but may fail to precisely recognize the occluded or invisible keypoints. Our RefineNet tries explicitly handling the "hard" keypoints by integrating all levels of feature representations from the Global-Net together with an online hard keypoint mining loss. In general, to address the multi-person pose estimation problem, a top-down pipeline is adopted to first generate a set of human bounding boxes based on a detector, followed by our CPN for keypoint localization in each human bounding box. Based on the proposed algorithm, we achieve stateof-art results on the COCO keypoint benchmark, with average precision at 73.0 on the COCO test-dev dataset and 72.1 on the COCO test-challenge dataset, which is a 19% relative improvement compared with 60.5 from the COCO 2016 keypoint challenge. Code 1 and the detection results are publicly available for further research.

Human evaluation is the foundation upon which the evaluation of both summarization systems and automatic metrics rests. However, existing human evaluation protocols and benchmarks for summarization either exhibit low inter-annotator agreement or lack the scale needed to draw statistically significant conclusions, and an in-depth analysis of human evaluation is lacking. In this work, we address the shortcomings of existing summarization evaluation along the following axes: 1) We propose a modified summarization salience protocol, Atomic Content Units (ACUs), which relies on fine-grained semantic units and allows for high inter-annotator agreement. 2) We curate the Robust Summarization Evaluation (RoSE) benchmark, a large human evaluation dataset consisting of over 22k summary-level annotations over state-of-the-art systems on three datasets. 3) We compare our ACU protocol with three other human evaluation protocols, underscoring potential confounding factors in evaluation setups. 4) We evaluate existing automatic metrics using the collected human annotations across evaluation protocols and demonstrate how our benchmark leads to more statistically stable and significant results. Furthermore, our findings have important implications for evaluating large language models (LLMs), as we show that LLMs adjusted by human feedback (e.g., GPT-3.5) may overfit unconstrained human evaluation, which is affected by the annotators' prior, input-agnostic preferences, calling for more robust, targeted evaluation methods.

Humans form mental images of 3D scenes to support counterfactual imagination, planning, and motor control. Our abilities to predict the appearance and affordance of the scene from previously unobserved viewpoints aid us in performing manipulation tasks (e.g., 6-DoF kitting) with a level of ease that is currently out of reach for existing robot learning frameworks. In this work, we aim to build artificial systems that can analogously plan actions on top of imagined images. To this end, we introduce Mental Imagery for Robotic Affordances (MIRA), an action reasoning framework that optimizes actions with novel-view synthesis and affordance prediction in the loop. Given a set of 2D RGB images, MIRA builds a consistent 3D scene representation, through which we synthesize novel orthographic views amenable to pixel-wise affordances prediction for action optimization. We illustrate how this optimization process enables us to generalize to unseen out-of-plane rotations for 6-DoF robotic manipulation tasks given a limited number of demonstrations, paving the way toward machines that autonomously learn to understand the world around them for planning actions.

In this paper, we examine the problem of visibility-aware robot navigation among movable obstacles (VANAMO). A variant of the well-known NAMO robotic planning problem, VANAMO puts additional visibility constraints on robot motion and object movability. This new problem formulation lifts the restrictive assumption that the map is fully visible and the object positions are fully known. We provide a formal definition of the VANAMO problem and propose the Look and Manipulate Backchaining (LaMB) algorithm for solving such problems. LaMB has a simple vision-based API that makes it more easily transferable to real-world robot applications and scales to the large 3D environments. To evaluate LaMB, we construct a set of tasks that illustrate the complex interplay between visibility and object movability that can arise in mobile base manipulation problems in unknown environments. We show that LaMB outperforms NAMO and visibility-aware motion planning approaches as well as simple combinations of them on complex manipulation problems with partial observability.

Recent improvements in conditional generative modeling have made it possible to generate high-quality images from language descriptions alone. We investigate whether these methods can directly address the problem of sequential decision-making. We view decision-making not through the lens of reinforcement learning (RL), but rather through conditional generative modeling. To our surprise, we find that our formulation leads to policies that can outperform existing offline RL approaches across standard benchmarks. By modeling a policy as a return-conditional diffusion model, we illustrate how we may circumvent the need for dynamic programming and subsequently eliminate many of the complexities that come with traditional offline RL. We further demonstrate the advantages of modeling policies as conditional diffusion models by considering two other conditioning variables: constraints and skills. Conditioning on a single constraint or skill during training leads to behaviors at test-time that can satisfy several constraints together or demonstrate a composition of skills. Our results illustrate that conditional generative modeling is a powerful tool for decision-making.

In this report, we present a fast and accurate object detection method dubbed DAMO-YOLO, which achieves higher performance than the state-of-the-art YOLO series. DAMO-YOLO is extended from YOLO with some new technologies, including Neural Architecture Search (NAS), efficient Reparameterized Generalized-FPN (RepGFPN), a lightweight head with AlignedOTA label assignment, and distillation enhancement. In particular, we use MAE-NAS, a method guided by the principle of maximum entropy, to search our detection backbone under the constraints of low latency and high performance, producing ResNet-like / CSP-like structures with spatial pyramid pooling and focus modules. In the design of necks and heads, we follow the rule of "large neck, small head". We import Generalized-FPN with accelerated queen-fusion to build the detector neck and upgrade its CSPNet with efficient layer aggregation networks (ELAN) and reparameterization. Then we investigate how detector head size affects detection performance and find that a heavy neck with only one task projection layer would yield better results. In addition, AlignedOTA is proposed to solve the misalignment problem in label assignment. And a distillation schema is introduced to improve performance to a higher level. Based on these new techs, we build a suite of models at various scales to meet the needs of different scenarios, i.e., DAMO-YOLO-Tiny/Small/Medium. They can achieve 43.0/46.8/50.0 mAPs on COCO with the latency of 2.78/3.83/5.62 ms on T4 GPUs respectively. The code is available at https://github.com/tinyvision/damo-yolo.