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(Photo)electrocatalysts capture sunlight and use it to drive chemical reactions such as water splitting to produce H2. A major factor limiting photocatalyst development is their large heterogeneity which spatially modulates reactivity and precludes establishing robust structure-function relationships. To make such links requires simultaneously probing of the electrochemical environment at microscopic length scales (nm to um) and broad timescales (ns to s). Here, we address this challenge by developing and applying in-situ steady-state and transient optical microscopies to directly map and correlate local electrochemical activity with hole lifetimes, oxygen vacancy concentration and the photoelectrodes crystal structure. Using this combined approach alongside spatially resolved X-Ray absorption measurements, we study microstructural and point defects in prototypical hematite (Fe2O3) photoanodes. We demonstrate that regions of Fe2O3, adjacent to microstructural cracks have a better photoelectrochemical response and reduced back electron recombination due to an optimal oxide vacancy concentration, with the film thickness and carbon impurities also dramatically influencing activity in a complex manner. Our work highlights the importance of microscopic mapping to understand activity and the impact of defects in even, seemingly, homogeneous solid-state metal oxide photoelectrodes.

3D posture estimation is important in analyzing and improving ergonomics in physical human-robot interaction and reducing the risk of musculoskeletal disorders. Vision-based posture estimation approaches are prone to sensor and model errors, as well as occlusion, while posture estimation solely from the interacting robot's trajectory suffers from ambiguous solutions. To benefit from the advantages of both approaches and improve upon their drawbacks, we introduce a low-cost, non-intrusive, and occlusion-robust multi-sensory 3D postural estimation algorithm in physical human-robot interaction. We use 2D postures from OpenPose over a single camera, and the trajectory of the interacting robot while the human performs a task. We model the problem as a partially-observable dynamical system and we infer the 3D posture via a particle filter. We present our work in teleoperation, but it can be generalized to other applications of physical human-robot interaction. We show that our multi-sensory system resolves human kinematic redundancy better than posture estimation solely using OpenPose or posture estimation solely using the robot's trajectory. This will increase the accuracy of estimated postures compared to the gold-standard motion capture postures. Moreover, our approach also performs better than other single sensory methods when postural assessment using RULA assessment tool., Comment: Submitted to ACM Transaction on Human-Robot ItneractionL: Special Issue on AI-HRI

Wearable robotic devices have potential to assist and protect their users. Toward design of a Smart Helmet, this article examines the effectiveness of audio and visual warnings to help participants brace for impacts. A user study examines different warnings and impacts applied to users while running. Perturbation forces scaled to user mass are applied from different directions and user displacement is measured to characterize effectiveness of the warning. This is accomplished using the TreadPort Active Wind Tunnel adapted to deliver forward, rearward, right, or left perturbation forces at precise moments during the locomotor cycle. The article presents an overview of the system and demonstrates the ability to precisely deliver consistent warnings and perturbations during gait. User study results highlight effectiveness of visual and audio warnings to help users brace for impact, resulting in guidelines that will inform future human-robot warning systems.

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., Comment: Submitted to IROS 2022. arXiv admin note: substantial text overlap with arXiv:2108.05971

Recent years have seen a rapidly escalating demand for battery technologies capable of storing more energy, charging more quickly and having longer usable lifetimes, driven largely by increased electrification of transport and by grid-scale energy storage systems. This has led to the development of many promising new electrode materials for high-rate lithium ion batteries. In order to rationalise and improve upon material performance, it is crucial to understand the fundamental ion-intercalation and degradation mechanisms occurring during realistic battery operation, on the nano- to meso-scale. Here we apply a straightforward laboratory-based operando optical scattering microscopy method to study micron-sized rods of the high-rate anode material Nb$_{14}$W$_3$O$_{44}$ during cycling at rates of up to 30C. We directly visualise an elongation of the rods, which, by comparison with ensemble X-ray diffraction, allows us to determine the state of charge (SOC) of the individual particle. A continuous change in scattering intensity with SOC is also seen, enabling observation of a non-equilibrium kinetic phase separation within individual particles. Phase field modelling (informed by pulsed-field-gradient nuclear magnetic resonance and electrochemical experiments) is used to verify the kinetic origin of this separation, which arises from a dependence of the Li-ion diffusion coefficient upon SOC. Finally, we witness how such intra-particle SOC heterogeneity can lead to particle cracking; we follow the cycling behaviour of the resultant fragments, and show that they may become electrically disconnected from the electrode. These results demonstrate the power of optical scattering microscopy to track rapid non-equilibrium processes, often occurring over less than 1 minute, which would be inaccessible with established characterisation techniques., Comment: 28 pages, 5 figures

Ergonomics and human comfort are essential concerns in physical human-robot interaction. Common practical methods in the area either fail in estimating the correct posture due to occlusion or suffer from inaccurate ergonomics models in performing postural optimization. We propose a novel alternative framework for posture estimation, assessment, and optimization for ergonomically intelligent physical human-robot interaction. We show that we can estimate human posture solely from the trajectory of the interacting robot with median deviation of 5 deg from motion capture. We propose DULA, a differentiable ergonomics assessment tool with 99.73% accuracy comparing to RULA. We use DULA in postural optimization for physical human-robot interaction tasks such as co-manipulation and teleoperation. We evaluate our framework through human and simulation experiments., Comment: Presented at AI-HRI symposium as part of AAAI-FSS 2021 (arXiv:2109.10836)

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. In order to enable efficient computation, previously proposed automated ergonomic assessment and correction tools make approximations or simplifications to gold-standard assessment tools used by ergonomists in practice. In order to retain assessment quality, while improving computational considerations, we introduce DULA, a differentiable and continuous ergonomics model learned to replicate the popular and scientifically validated RULA assessment. We show that DULA provides assessment comparable to RULA while providing computational benefits. We highlight DULA's strength in a demonstration of gradient-based postural optimization for a simulated teleoperation task.

Despite years of research into patient falls in hospital rooms, falls and related injuries remain a serious concern to patient safety. In this work, we formulate a gradient-free constrained optimization problem to generate and reconfigure the hospital room interior layout to minimize the risk of falls. We define a cost function built on a hospital room fall model that takes into account the supportive or hazardous effect of the patient's surrounding objects, as well as simulated patient trajectories inside the room. We define a constraint set that ensures the functionality of the generated room layouts in addition to conforming to architectural guidelines. We solve this problem efficiently using a variant of simulated annealing. We present results for two real-world hospital room types and demonstrate a significant improvement of 18% on average in patient fall risk when compared with a traditional hospital room layout and 41% when compared with randomly generated layouts., Comment: Accepted in: "10th International Conference on Operations Research and Enterprise Systems". 13 pages, 10 figures

Key to advancing lithium-ion battery technology, and in particular fast charging capabilities, is our ability to follow and understand the dynamic processes occurring in operating materials under realistic conditions, in real time, and on the nano- to meso-scale. Currently, operando imaging of lithium-ion dynamics requires sophisticated synchrotron X-ray or electron microscopy techniques, which do not lend themselves to high-throughput material screening. This limits rapid and rational materials improvements. Here we introduce a simple lab-based, optical interferometric scattering microscope to resolve nanoscopic lithium-ion dynamics in battery materials and apply it to follow the repeated cycling of the archetypical cathode material Li$_\textit{x}$CoO$_2$. The method allows us to visualise directly the insulator-metal, solid solution and lithium ordering phase transitions in this material. We determine rates of lithium insertion and removal at the single-particle level and identify different mechanisms that occur on charge vs. discharge. Finally, we capture the dynamic formation of domain boundaries between different crystal orientations associated with the monoclinic lattice distortion at around Li$_{0.5}$CoO$_2$. The high throughput nature of our methodology allows many particles to be sampled across the entire electrode and, moving forward, will enable exploration of the role of dislocations, morphologies and cycling rate on battery degradation. The generality of our imaging concept means that it can be applied to study any battery electrode, and more broadly, systems where the transport of ions is associated with electronic or structural changes, including nanoionic films, ionic conducting polymers, photocatalytic materials and memristors., Comment: 18 pages, including 5 figures

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., Comment: 7 pages + 2 page supplementary

Objectives: The aims of this study are to identify factors in physical environments that contribute to patient falls in hospitals and to propose a computational model to evaluate patient room designs. Background: The existing fall risk assessment tools have an acceptable level of sensitivity and specificity, however, they only consider intrinsic factors and medications, making the prediction very limited in terms of how the physical environment contributes to fall risk. Methods: We provide a computational model for risk of fall based on physical-environment and patient-motion factors. We use a trajectory optimization approach for patient motion prediction. Results: We run the proposed model on four room designs as examples of various room design categories. Results show the capabilities of the proposed model in identifying risky locations within the room. Conclusions: Our study shows the potential capabilities of the proposed model. Due to lack of enough evidence for the examined factors, it is not possible at this point to gain robust confidence in the final evaluations. More studies using quantitative, relational, or causal designs are recommended to inform the proposed model for patient falls. Application: Developing a comprehensive fall risk model is a significant step in understanding and solving the problem of patient falls in hospitals. It can provide guidance for healthcare decision makers to optimize effective interventions to reduce risk of falls while promoting safe patient mobility in the hospital room environment. We can also use it in healthcare technologies such as assistive robots to provide informed assistance.

Ergonomic assessment of human posture plays a vital role in understanding work-related safety and health. Current posture estimation approaches face occlusion challenges in teleoperation and physical human-robot interaction. We investigate if the leader robot is an adequate sensor for posture estimation in teleoperation and we introduce a new probabilistic approach that relies solely on the trajectory of the leader robot for generating observations. We model the human using a redundant, partially-observable dynamical system and we infer the posture using a standard particle filter. We compare our approach with postures from a commercial motion capture system and also two least-squares optimization approaches for human inverse kinematics. The results reveal that the proposed approach successfully estimates human postures and ergonomic risk scores comparable to those estimates from gold-standard motion capture., Comment: Submitted to IEEE CASE 2021

A particular type of assistive robots designed for physical interaction with objects could play an important role assisting with mobility and fall prevention in healthcare facilities. Autonomous mobile manipulation presents a hurdle prior to safely using robots in real life applications. In this article, we introduce a mobile manipulation framework based on model predictive control using learned dynamics models of objects. We focus on the specific problem of manipulating legged objects such as those commonly found in healthcare environments and personal dwellings (e.g. walkers, tables, chairs). We describe a probabilistic method for autonomous learning of an approximate dynamics model for these objects. In this method, we learn dynamic parameters using a small dataset consisting of force and motion data from interactions between the robot and object. Moreover, we account for multiple manipulation strategies by formulating the manipulation planning as a mixed-integer convex optimization. The proposed framework considers the hybrid control system comprised of i) choosing which leg to grasp, and ii) control of continuous applied forces for manipulation. We formalize our algorithm based on model predictive control to compensate for modeling errors and find an optimal path to manipulate the object from one configuration to another. We show results for several objects with various wheel configurations. Simulation and physical experiments show that the obtained dynamics models are sufficiently accurate for safe and collision-free manipulation. When combined with the proposed manipulation planning algorithm, the robot successfully moves the object to a desired pose while avoiding collision.