Collaborative Research: CPS: Small: Risk-Aware Planning and Control for Safety-Critical Human-CPS

合作研究：CPS：小型：安全关键型人类 CPS 的风险意识规划和控制

• 批准号: 2218760
• 负责人: Dorsa Sadigh
• 金额: $ 25万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2218760&HistoricalAwards=false
• 关键词: Collaborative; Research; CPS; Small; Risk

The future of cyber-physical systems are smart technologies that can work collaboratively, cooperatively, and safely with humans. Smart technologies and humans will share autonomy, i.e., the right, obligation and ability to share control in order to meet their mutual objectives in the environment of operations. For example, surgical robots must interact with surgeons to increase their capabilities in performing high-precision surgeries, drones need to deliver packages to humans and places, and autonomous cars need to share roads with human-driven cars. In all such interactions, these systems must act safely despite the risks and uncertainties that are intrinsic with humans, technologies, and the environments in which they interact. The key insight of this project is that control strategies can be developed that increase safety in situations where a human needs to closely interact with a cyber-physical system (CPS) that is capable of autonomy or semi-autonomous action.The goal of this project is to develop risk-aware interactive control and planning for achieving safe cyber-physical-human (CPS-h) systems. This project will advance the state-of-the-art of CPS-h planning and control in three main ways: (i) developing computationally tractable risk-aware trajectory planning algorithms that are suited to general autonomous CPS-h, (ii) developing a computationally efficient and empirically supported framework to account for risk-awareness in human’s decision-making, and (iii) deriving interaction-aware planning algorithms for achieving safe and efficient interactions between multiple risk-aware agents. The proposed algorithms will be extensively evaluated with human subjects in interaction with autonomous CPS-h such as autonomous cars and quadcopters. This work will have direct impact on many CPS-h domains including but not limited to multi-agent interactions, autonomous driving, collaboration and coordination between humans and autonomous agents in safety-critical scenarios.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

网络物理系统的未来是智能技术，可以与人类合作，合作和安全地工作。智能技术和人类将共享自主权，即共享控制权的正确，客观和能力，以实现其在操作环境中的相互目标。例如，手术机器人必须与外科医生进行互动，以提高其进行高精度手术的能力，无人机需要将包裹运送到人类和地方，并且自动驾驶汽车需要与人类驱动的汽车共享道路。在所有这些相互作用中，这些系统必须安全地采取与人类，技术及其相互作用的环境内在的风险和不确定性。该项目的关键见解是，可以在人类需要与能够与能够自治或半自治作用的网络物理系统（CPS）紧密互动的情况下提高安全性的控制策略。该项目的目的是开发风险意识的交互式控制和计划以实现安全的互动控制和计划，以实现安全的网络物理 - 物理 - 物理 - 人类（CPS-cps-h）系统。 This project will advance the state-of-the-art of CPS-h planning and control in three main ways: (i) developing computationally tractable risk-aware trajectory planning algorithms that are suited to general autonomous CPS-h, (ii) developing a computationally efficient and empirically supported framework to account for risk-awareness in human’s decision-making, and (iii) deriving interaction-aware planning algorithms for achieving safe and efficient多种风险感知代理之间的相互作用。所提出的算法将通过与自主CPS-H相互作用的人类受试者（例如自动驾驶汽车和四轮驱动器）进行广泛评估。这项工作将对许多CPS-H领域产生直接影响，包括但不限于在安全至关重要的情况下人类和自主代理之间的多代理互动，自主驾驶，协作和协调。该奖项反映了NSF的法定任务，并通过使用该基金会的智力功能和广泛的影响来评估NSF的法定任务，并被认为是珍贵的支持。

项目成果

HYDRA: Hybrid Robot Actions for Imitation Learning

• DOI: 10.48550/arxiv.2306.17237
• 发表时间: 2023-06
• 作者: Suneel Belkhale;Yuchen Cui;Dorsa Sadigh

Eliciting Compatible Demonstrations for Multi-Human Imitation Learning

• DOI: 10.48550/arxiv.2210.08073
• 发表时间: 2022-10
• 作者: Kanishk Gandhi;Siddharth Karamcheti;Madeline Liao;Dorsa Sadigh

Toward Grounded Commonsense Reasoning

• 发表时间: 2023-06
• 作者: Minae Kwon;Hengyuan Hu;Vivek Myers;Siddharth Karamcheti;A. Dragan;Dorsa Sadigh

Learning Sequential Acquisition Policies for Robot-Assisted Feeding

• DOI: 10.48550/arxiv.2309.05197
• 发表时间: 2023-09
• 作者: Priya Sundaresan;Jiajun Wu;Dorsa Sadigh

Gesture-Informed Robot Assistance via Foundation Model

通过基础模型提供手势通知机器人辅助

• 发表时间: 2023
• 期刊: Proceedings of the 7th Conference on Robot Learning (CoRL
• 作者: Lin, Li-Heng;Cui, Yuchen;Hao, Yilun;Xia, Fei;Sadigh, Dorsa
• 通讯作者: Sadigh, Dorsa