长期以来，Robotics一直是一个遍布复杂系统体系结构的领域，无论传统或基于学习的模块和联系都需要大量的人类专业知识和先验知识。受大型预训练语言模型的启发，这项工作引入了预先培训的通用表示范式，该范式可以作为给定机器人多个任务的起点。我们提出了感知性因果变压器（PACT），这是一种基于生成变压器的架构，旨在以自我监督的方式直接从机器人数据构建表示形式。通过对状态和行动的自动回归预测，我们的模型隐含地编码了特定机器人的动态和行为。我们的实验评估重点是移动药物的域，我们表明该机器人特定的表示可以作为单个起点，以实现不同的任务，例如安全导航，定位和映射。我们评估了两个形式：使用激光雷达传感器作为感知输入（MUSHR）的轮式机器人，以及使用第一人称RGB图像（栖息地）的模拟药物。我们表明，与训练单个模型的同时训练单个模型相比，对所有任务的单个模型进行训练，并且与独立培训单独的大型模型相当的性能，对每个任务的单个模型进行了可比的训练，则在较大的审计模型上进行了固定小型任务特异性网络，从而使性能明显提高。通过跨任务共享共同的优质表示，我们可以降低整体模型容量并加快此类系统的实时部署。

模拟逼真的传感器是自主系统数据生成的挑战，通常涉及精心手工的传感器设计，场景属性和物理建模。为了减轻这一点，我们引入了一条管道，用于对逼真的激光雷达传感器进行数据驱动的模拟。我们提出了一个模型，该模型可以在RGB图像和相应的LIDAR功能（例如Raydrop或每点强度）之间直接从真实数据集中进行映射。我们表明，我们的模型可以学会编码逼真的效果，例如透明表面上的掉落点或反射材料上的高强度回报。当应用于现成的模拟器软件提供的天真播放点云时，我们的模型通过根据场景的外观预测强度和删除点来增强数据，以匹配真实的激光雷达传感器。我们使用我们的技术来学习两个不同的LIDAR传感器的模型，并使用它们相应地改善模拟的LiDAR数据。通过车辆细分的示例任务，我们表明通过我们的技术增强模拟点云可以改善下游任务性能。

自然语言是表达人类意图的最直观的方式之一。但是，将指示和命令转换为机器人运动生产以及在现实世界中的部署，远非一件容易的事。的确，将机器人的固有的低水平几何形状和运动动力学约束与人类的高级语义信息相结合，振奋人心，并提出了对任务设计问题的新挑战 - 通常会通过一组静态的动作目标和命令来实现任务或硬件特定的解决方案。相反，这项工作提出了一个灵活的基于语言的框架，该框架允许使用有关先前任务或机器人信息的限制的语言命令修改通用3D机器人轨迹。通过利用预训练的语言模型，我们使用自动回归变压器将自然语言输入和上下文图像映射到3D轨迹中的变化中。我们通过模拟和现实生活实验表明，该模型可以成功遵循人类的意图，从而改变了多个机器人平台和环境的轨迹的形状和速度。这项研究迈出了建立机器人技术的大型预训练的基础模型的一步，并展示了这样的模型如何在人与机器之间建立更直观，更灵活的相互作用。代码库可在以下网址提供：https：//github.com/arthurfenderbucker/nl_traimptory_reshaper。

强化学习（RL）和连续的非线性控制已成功部署在复杂的顺序决策任务的多个领域中。但是，鉴于学习过程的探索性质和模型不确定性的存在，由于缺乏安全保证，将它们应用于安全至关重要的控制任务是一项挑战。另一方面，尽管将控制理论方法与学习算法相结合，但在安全RL应用中显示了希望，但安全数据收集过程的样本效率尚未得到很好的解决。在本文中，我们提出了一个\ emph {可证明的}示例有效的情节安全学习框架，用于在线控制任务，以利用未知的非线性动力学系统来利用安全的探索和剥削。特别是，框架1）在随机设置中扩展控制屏障功能（CBF），以在模型学习过程中实现可证明的高概率安全性，2）整合基于乐观的探索策略，以有效地将安全探索过程与学习的动态有效地指导安全探索过程对于\ emph {接近最佳}控制性能。我们对与理论保证的最佳控制器和概率安全性的偶发性遗憾进行了正式分析。提供了仿真结果以证明所提出算法的有效性和效率。

Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process. Also, in order to utilize recent advances in machine intelligence and deep learning we need to collect a large amount of annotated training data in a variety of conditions and environments. We present a new simulator built on Unreal Engine that offers physically and visually realistic simulations for both of these goals. Our simulator includes a physics engine that can operate at a high frequency for real-time hardware-in-the-loop (HITL) simulations with support for popular protocols (e.g. MavLink). The simulator is designed from the ground up to be extensible to accommodate new types of vehicles, hardware platforms and software protocols. In addition, the modular design enables various components to be easily usable independently in other projects. We demonstrate the simulator by first implementing a quadrotor as an autonomous vehicle and then experimentally comparing the software components with real-world flights.

3D object detection is vital as it would enable us to capture objects' sizes, orientation, and position in the world. As a result, we would be able to use this 3D detection in real-world applications such as Augmented Reality (AR), self-driving cars, and robotics which perceive the world the same way we do as humans. Monocular 3D Object Detection is the task to draw 3D bounding box around objects in a single 2D RGB image. It is localization task but without any extra information like depth or other sensors or multiple images. Monocular 3D object detection is an important yet challenging task. Beyond the significant progress in image-based 2D object detection, 3D understanding of real-world objects is an open challenge that has not been explored extensively thus far. In addition to the most closely related studies.

Recent methods demonstrate that data augmentation using counterfactual knowledge can teach models the causal structure of a task, leading to robust and generalizable models. However, such counterfactual data often has a limited scale and diversity if crowdsourced and is computationally expensive to extend to new perturbation types if generated using supervised methods. To address this, we introduce a new framework called DISCO for automatically generating high-quality counterfactual data at scale. DISCO engineers prompts to generate phrasal perturbations with a large general language model. Then, a task-specific teacher model filters the generation to distill high-quality counterfactual data. We show that learning with this counterfactual data yields a comparatively small student model that is 6% (absolute) more robust and generalizes 5% better across distributions than baselines on various challenging evaluations. This model is also 15% more sensitive in differentiating original and counterfactual examples, on three evaluation sets written by human workers and via human-AI collaboration.

Recent work has shown that large language models are capable of generating natural language reasoning steps or Chains-of-Thoughts (CoT) to answer a multi-step question when prompted to do so. This is insufficient, however, when the necessary knowledge is not available or up-to-date within a model's parameters. A straightforward approach to address this is to retrieve text from an external knowledge source using the question as a query and prepend it as context to the model's input. This, however, is also insufficient for multi-step QA where \textit{what to retrieve} depends on \textit{what has already been derived}. To address this issue we propose IRCoT, a new approach that interleaves retrieval with CoT for multi-step QA, guiding the retrieval with CoT and in turn using retrieved results to improve CoT. Our experiments with GPT3 show substantial improvements in retrieval (up to 22 points) and downstream QA (up to 16 points) over the baselines on four datasets: HotpotQA, 2WikiMultihopQA, MuSiQue, and IIRC. Notably, our method also works well for much smaller models such as T5-Flan-large (0.7B) without any additional training.

Voice assistants are deployed widely and provide useful functionality. However, recent work has shown that commercial systems like Amazon Alexa and Google Home are vulnerable to voice-based confusion attacks that exploit design issues. We propose a systems-oriented defense against this class of attacks and demonstrate its functionality for Amazon Alexa. We ensure that only the skills a user intends execute in response to voice commands. Our key insight is that we can interpret a user's intentions by analyzing their activity on counterpart systems of the web and smartphones. For example, the Lyft ride-sharing Alexa skill has an Android app and a website. Our work shows how information from counterpart apps can help reduce dis-ambiguities in the skill invocation process. We build SkilIFence, a browser extension that existing voice assistant users can install to ensure that only legitimate skills run in response to their commands. Using real user data from MTurk (N = 116) and experimental trials involving synthetic and organic speech, we show that SkillFence provides a balance between usability and security by securing 90.83% of skills that a user will need with a False acceptance rate of 19.83%.

Recent advances in batch (offline) reinforcement learning have shown promising results in learning from available offline data and proved offline reinforcement learning to be an essential toolkit in learning control policies in a model-free setting. An offline reinforcement learning algorithm applied to a dataset collected by a suboptimal non-learning-based algorithm can result in a policy that outperforms the behavior agent used to collect the data. Such a scenario is frequent in robotics, where existing automation is collecting operational data. Although offline learning techniques can learn from data generated by a sub-optimal behavior agent, there is still an opportunity to improve the sample complexity of existing offline reinforcement learning algorithms by strategically introducing human demonstration data into the training process. To this end, we propose a novel approach that uses uncertainty estimation to trigger the injection of human demonstration data and guide policy training towards optimal behavior while reducing overall sample complexity. Our experiments show that this approach is more sample efficient when compared to a naive way of combining expert data with data collected from a sub-optimal agent. We augmented an existing offline reinforcement learning algorithm Conservative Q-Learning with our approach and performed experiments on data collected from MuJoCo and OffWorld Gym learning environments.