Ergonomics and human comfort are essential concerns in physical human-robot interaction applications. Defining an accurate and easy-to-use ergonomic assessment model stands as an important step in providing feedback for postural correction to improve operator health and comfort. Common practical methods in the area suffer from inaccurate ergonomics models in performing postural optimization. In order to retain assessment quality, while improving computational considerations, we propose a novel framework for postural assessment and optimization for ergonomically intelligent physical human-robot interaction. We introduce DULA and DEBA, differentiable and continuous ergonomics models learned to replicate the popular and scientifically validated RULA and REBA assessments with more than 99% accuracy. We show that DULA and DEBA provide assessment comparable to RULA and REBA while providing computational benefits when being used in postural optimization. We evaluate our framework through human and simulation experiments. We highlight DULA and DEBA's strength in a demonstration of postural optimization for a simulated pHRI task.

Planning under uncertainty is a crucial capability for autonomous systems to operate reliably in uncertain and dynamic environments. The concern of safety becomes even more critical in healthcare settings where robots interact with human patients. In this paper, we propose a novel risk-aware planning framework to minimize the risk of falls by providing a patient with an assistive device. Our approach combines learning-based prediction with model-based control to plan for the fall prevention task. This provides advantages compared to end-to-end learning methods in which the robot's performance is limited to specific scenarios, or purely model-based approaches that use relatively simple function approximators and are prone to high modeling errors. We compare various risk metrics and the results from simulated scenarios show that using the proposed cost function, the robot can plan interventions to avoid high fall score events.

One of the most concerning and costly problems in hospitals is patients falls. We address this problem by introducing PAM, a patient assistant mobile robot, that maneuvers mobility aids to assist with fall prevention. Common objects found inside hospitals include objects with legs (i.e. walkers, tables, chairs, equipment stands). For a mobile robot operating in such environments, safely maneuvering these objects without collision is essential. Since providing the robot with dynamic models of all possible legged objects that may exist in such environments is not feasible, autonomous learning of an approximate dynamic model for these objects would significantly improve manipulation planning. We describe a probabilistic method to do this by fitting pre-categorized object models learned from minimal force and motion interactions with an object. In addition, we account for multiple manipulation strategies, which requires a hybrid control system comprised of discrete grasps on legs and continuous applied forces. To do this, we use a simple one-wheel point-mass model. A hybrid MPC-based manipulation planning algorithm was developed to compensate for modeling errors. While the proposed algorithm applies to a broad range of legged objects, we only show results for the case of a 2-wheel, 4-legged walker in this paper. Simulation and experimental tests show that the obtained dynamic model is sufficiently accurate for safe and collision-free manipulation. When combined with the proposed manipulation planning algorithm, the robot can successfully move the object to a desired position without collision.