CRII: CHS: Enabling Safe and Adaptive Robot-aided Gait Training through Biomechanical Characterization and Learning from Demonstration

由于与年龄相关的神经疾病，老年人口的空前增长对步态康复产生了很高的需求。为了满足这一迫切需求，各种辅助机器人被开发出来以改善步态训练结果，阻抗控制(控制环境产生的外部运动的阻力)已被广泛应用于这些机器人中，以确保人与机器人的安全交互。然而，由于人类神经和肌肉骨骼动力学的复杂性质，很难个性化这类机器人的虚拟阻抗。另一方面，理疗师可以根据实时感觉反馈和临床经验，在步态周期的正确时刻为患者提供适应性帮助。受此启发，人们可以想象通过学习治疗师的演示来设计辅助机器人控制系统，但这种纯粹的数据驱动的方法可能会导致新步态模式的性能显著下降，这给用户带来安全风险。本研究将开发一种混合辅助机器人控制方法，将基于模型的阻抗控制与从治疗师的行为中学习的机器学习相结合，从而使得到的机器人辅助既安全又自适应。项目成果将包括一个用于人-机器人物理协作的新算法框架，该框架由于基于模型的控制和由机器人学习产生的智能适应而显示出性能保证。这项新技术将在许多其他安全关键的人-机器人协作场景中有广泛的应用，包括协同制造、(半)自动驾驶和服务机器人。通过将研究与教育活动紧密结合起来，这项工作的更广泛影响将得到进一步加强，包括在现有机器人课程中开设新模块，为来自代表性不足群体的本科生提供研究机会，以及为当地高中生提供实习机会。这项工作的科学贡献将包括：1)整合不同种类的可穿戴传感器数据以建立来自治疗师演示的机器人学习模型，以及人类膝关节阻抗表征以建立机器人阻抗控制模型；2)基于从演示和阻抗控制中学习的融合的机器人规划方法，其权重由机器人学习模型中的置信度确定；以及3)自动请求新的演示数据并结合主题反馈来改进机器人学习和阻抗控制模型。该方法的性能将在生物力学模拟、健康受试者的实验室测试以及中风和帕金森病患者的初步研究中进行评估。据设想，项目成果将使辅助机器人高度智能化，从而使治疗师可以同时甚至远程与多名患者合作，这将显著降低治疗师的劳动强度和患者康复训练的成本。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The unprecedented growth in the elderly population is generating a high demand for gait rehabilitation due to age-related neurological diseases. To address this urgent need various assistive robots have been developed to improve gait training outcomes, and impedance control (controlling the force of resistance to external motions that are produced by the environment) has been widely employed in these robots to ensure safe human-robot interaction. However, it is difficult to personalize the virtual impedance for such robots due to the complex nature of human neurological and musculoskeletal dynamics. On the other hand, a physical therapist can provide adaptive assistance to a patient at the correct moment in a gait cycle based on real-time sensory feedback and clinical experience. Inspired by this observation, one could imagine designing an assistive robot control system by learning from therapists' demonstrations, but such a purely data-driven approach could lead to significantly degraded performance with new gait patterns, which creates safety risks for users. This research will develop a hybrid assistive robot control approach, which integrates model-based impedance control with machine learning from therapists' behaviors so that the resultant robot assistance is safe yet adaptive. Project outcomes will include a novel algorithm framework for physical human-robot collaboration that exhibits both performance guarantees due to the model-based control and intelligent adaptation resulting from robot learning. The new technology will have a wide range of applications in many other safety-critical human-robot collaboration scenarios, including collaborative manufacturing, (semi) autonomous driving, and service robots. The broader impacts of the work will be further enhanced by tight integration of the research with educational activities including new modules in existing robotics classes, research opportunities for undergraduate students from underrepresented groups, and internships for local high-school students. The scientific contribution of the work will include: 1) integration of heterogeneous wearable sensor data to build the robot learning model from therapists' demonstrations, and human knee impedance characterization to build the robot impedance control model; 2) a robot planning approach based on a fusion of learning from demonstration and impedance control, with the weights determined by the degree of confidence in the robot learning model; and 3) automatic requests for new demonstration data and incorporation of subject feedback to refine both the robot learning and impedance control models. Performance of the approach will be assessed in biomechanical simulations, in lab tests with healthy subjects, and in a pilot study with stroke and Parkinson's disease patients. It is envisioned that project outcomes will make assistive robots highly intelligent so that a therapist could work with multiple patients simultaneously and even remotely, which could significantly reduce both the therapists' labor intensity and cost of rehabilitation training for patients.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.