面向人类伴侣机器人的自适应交互策略：基于深度强化学习
Towards Adaptive Interaction Strategies for Human Companion Robot via Deep Reinforcement Learning
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中文总结 AI 辅助
本研究提出基于深度强化学习的自适应人机陪伴策略，结合MPPI与CBF控制，使机器人动态调整跟踪位置，在室内外实验中成功率与精度分别提升至少24%和47%。
中文摘要 AI 辅助
在人机交互（HRI）领域，在真实环境中实现灵活的人类陪伴具有巨大的应用潜力，但也带来了重大挑战。传统方法通常将机器人限制在相对于人类的固定位置，例如从后方、前方或并排跟踪，这限制了机器人在动态工作空间中的适应性。本研究提出了一种新颖的人类陪伴策略，利用深度强化学习（DRL）使移动机器人能够根据变化的条件动态调整其跟踪位置。定义了一个交互空间来捕捉人与机器人之间的关系，同时考虑环境因素，该空间作为DRL中状态空间的基础，以帮助机器人适应环境变化。通过将模型预测路径积分（MPPI）控制与控制障碍函数（CBF）相结合，开发了一种人机陪伴控制器，确保机器人在位置和方向上都准确跟踪目标的运动，同时避开障碍物并增强社会接受度和安全性。所提出的方法在室内和室外的真实场景中进行了评估，并与其他研究进行了比较。结果表明，所提出的方法将成功率和跟踪精度分别提高了至少24%和47%，同时增强了人类舒适度。实验证明了机器人能够以高达1.7米/秒的速度灵活陪伴行走的人，动态调整其策略而不受固定位置的限制。此外，机器人尊重人类的亲密空间，以确保安全、舒适和有效的避障。
英文摘要
In the field of Human-Robot Interaction (HRI), achieving flexibility in human-accompanying within real-world environments holds great potential for various applications but also poses significant challenges. Traditional methods typically restrict robots to fixed positions relative to humans, such as tracking from behind, in front, or side-by-side, which limits robot adaptability in dynamic workspaces. This study introduces a novel human-companioning strategy that uses Reinforcement Learning (DRL) to enable mobile robots to dynamically adjust their tracking positions according to varying conditions. An interaction space is defined to capture the relationship between the human and the robot while considering the environment, which serves as the basis for state spaces in DRL to assist the robot in adapting to environmental changes. A human-robot companion controller is developed by integrating Model Predictive Path Integral (MPPI) control with Control Barrier Functions (CBF), ensuring that the robot accurately follows the target's movement in both position and orientation while avoiding obstacles and enhancing social acceptance and safety. The proposed approach is evaluated in real-world scenarios, both indoors and outdoors, and compared with other studies. The results show that the proposed method improves the success rate and tracking accuracy by at least 24% and 47%, respectively, while enhancing human comfort. Experiments demonstrate the robot's ability to flexibly accompany a person walking at speeds of up to 1.7 m/s, dynamically adjusting its strategy without being confined to a fixed position. Additionally, the robot respects the human's intimate space to ensure safety, comfort, and effective obstacle avoidance.
发表机构
- National Cheng Kung University（国立成功大学）
机构由 AI 辅助整理，请以论文原文为准。
