J Biomech

Despite the prevalence of directional changes during every-day gait, relatively little is known about turning compared to straight gait. While the center of mass (COM) movement during straight gait is well characterized, the COM trajectory and the factors that influence it are less established for turning. This study investigated the influence of a corner 7\ub3s height on the COM trajectory as participants walked around the corner. Ten participants (25.3\uc2\ub13.74 years) performed both 90\uc2\ub0 step and spin turns to the left at self-selected slow, normal, and fast speeds while walking inside a marked path. A pylon was placed on the inside corner of the path. Four different pylon heights were used to correspond to heights of everyday objects: 0 cm (no object), 63 cm (box, crate), 104 cm (desk, table, counter), 167 cm (shelf, cabinet). Obstacle height was found to significantly affect the COM trajectory. Taller obstacles resulted in more distance between the corner and the COM, and between the corner and the COP. Taller obstacles also were associated with greater curvature in the COM trajectory, indicating a smaller turning radius despite the constant 90\uc2\ub0 corner. Taller obstacles correlated to an increased required coefficient of friction (RCOF) due to the smaller turning radii. Taller obstacles also tended towards greater mediolateral (ML) COM-COP angles, contrary to the initial hypothesis. Additionally, the COM was found to remain outside the base of support (BOS) for the entire first half of stance phase for all conditions indicating a high risk of falls resulting from slips.L30 AG022963/AG/NIA NIH HHS/United StatesR01-OH009222/OH/NIOSH CDC HHS/United States2016-01-02T00:00:00Z25468662PMC427422

Neural and behavioral bases of innate behaviors

Recently, ethological studies of animal behavior uncovered its complexity while neuroscientific work began unraveling the neural bases of behavior. Improvements in algorithmic understanding of behavior and neural function contributed to re- cent breakthroughs in robotics and artificial intelligence systems. Yet, animals’ decision-making and motor-control are unequalled by human engineered systems and the continued investigation of the behavioral and neural bases of these abilities is crucial for understanding brain function and inform further technological devel- opments. In my PhD work, I first investigate escape path selection in mice presented with threat, demonstrating how mice combined rapidly acquired spatial knowledge with an innate choice heuristic to inform decision-making. This strategy minimizes the requirement for trial-and-error learning and yields accurate decision-making by combining knowledge acquired at an evolutionarily time-scale with that acquired by the individual. Future work aimed at understanding how these sources of in- formation are combined in the brain to inform decision-making may lead to more efficient artificial learning agents. Next, I studied goal-directed locomotion behav- ior in which mice move rapidly through an environment to reach a goal location. Successful goal-directed locomotion behavior requires substantial navigation and motor control skills and, additionally, sophisticated planning and control of move- ments while moving at high speed. Detailed behavioral quantification and compar- ison to a control-theoretic model demonstrated that mice do possess such planning skills, allowing them to execute rapid and efficient trajectories to a goal. Population- level extracellular recordings of neural activity during goal directed locomotion was also used to begin uncovering the neural bases of planning during locomotion. Altogether, my work combined accurate quantification of animal movements with the- oretical models of optimal behavior to understand behavior at a computation level, aiming to provide crucial information to inform future studies on the neural bases of innate behaviors and aid in the development of novel artificial learning system

Human-aware space sharing and navigation for an interactive robot

Les méthodes de planification de mouvements robotiques se sont développées à un rythme accéléré ces dernières années. L'accent a principalement été mis sur le fait de rendre les robots plus efficaces, plus sécurisés et plus rapides à réagir à des situations imprévisibles. En conséquence, nous assistons de plus en plus à l'introduction des robots de service dans notre vie quotidienne, en particulier dans les lieux publics tels que les musées, les centres commerciaux et les aéroports. Tandis qu'un robot de service mobile se déplace dans l'environnement humain, il est important de prendre en compte l'effet de son comportement sur les personnes qu'il croise ou avec lesquelles il interagit. Nous ne les voyons pas comme de simples machines, mais comme des agents sociaux et nous nous attendons à ce qu'ils se comportent de manière similaire à l'homme en suivant les normes sociétales comme des règles. Ceci a créé de nouveaux défis et a ouvert de nouvelles directions de recherche pour concevoir des algorithmes de commande de robot, qui fournissent des comportements de robot acceptables, lisibles et proactifs. Cette thèse propose une méthode coopérative basée sur l'optimisation pour la planification de trajectoire et la navigation du robot avec des contraintes sociales intégrées pour assurer des mouvements de robots prudents, conscients de la présence de l'être humain et prévisibles. La trajectoire du robot est ajustée dynamiquement et continuellement pour satisfaire ces contraintes sociales. Pour ce faire, nous traitons la trajectoire du robot comme une bande élastique (une construction mathématique représentant la trajectoire du robot comme une série de positions et une différence de temps entre ces positions) qui peut être déformée (dans l'espace et dans le temps) par le processus d'optimisation pour respecter les contraintes données. De plus, le robot prédit aussi les trajectoires humaines plausibles dans la même zone d'exploitation en traitant les chemins humains aussi comme des bandes élastiques. Ce système nous permet d'optimiser les trajectoires des robots non seulement pour le moment présent, mais aussi pour l'interaction entière qui se produit lorsque les humains et les robots se croisent les uns les autres. Nous avons réalisé un ensemble d'expériences avec des situations interactives humains-robots qui se produisent dans la vie de tous les jours telles que traverser un couloir, passer par une porte et se croiser sur de grands espaces ouverts. La méthode de planification coopérative proposée se compare favorablement à d'autres schémas de planification de la navigation à la pointe de la technique. Nous avons augmenté le comportement de navigation du robot avec un mouvement synchronisé et réactif de sa tête. Cela permet au robot de regarder où il va et occasionnellement de détourner son regard vers les personnes voisines pour montrer que le robot va éviter toute collision possible avec eux comme prévu par le planificateur. À tout moment, le robot pondère les multiples critères selon le contexte social et décide de ce vers quoi il devrait porter le regard. Grâce à une étude utilisateur en ligne, nous avons montré que ce mécanisme de regard complète efficacement le comportement de navigation ce qui améliore la lisibilité des actions du robot. Enfin, nous avons intégré notre schéma de navigation avec un système de supervision plus large qui peut générer conjointement des comportements du robot standard tel que l'approche d'une personne et l'adaptation de la vitesse du robot selon le groupe de personnes que le robot guide dans des scénarios d'aéroport ou de musée.The methods of robotic movement planning have grown at an accelerated pace in recent years. The emphasis has mainly been on making robots more efficient, safer and react faster to unpredictable situations. As a result we are witnessing more and more service robots introduced in our everyday lives, especially in public places such as museums, shopping malls and airports. While a mobile service robot moves in a human environment, it leaves an innate effect on people about its demeanor. We do not see them as mere machines but as social agents and expect them to behave humanly by following societal norms and rules. This has created new challenges and opened new research avenues for designing robot control algorithms that deliver human-acceptable, legible and proactive robot behaviors. This thesis proposes a optimization-based cooperative method for trajectoryplanning and navigation with in-built social constraints for keeping robot motions safe, human-aware and predictable. The robot trajectory is dynamically and continuously adjusted to satisfy these social constraints. To do so, we treat the robot trajectory as an elastic band (a mathematical construct representing the robot path as a series of poses and time-difference between those poses) which can be deformed (both in space and time) by the optimization process to respect given constraints. Moreover, we also predict plausible human trajectories in the same operating area by treating human paths also as elastic bands. This scheme allows us to optimize the robot trajectories not only for the current moment but for the entire interaction that happens when humans and robot cross each other's paths. We carried out a set of experiments with canonical human-robot interactive situations that happen in our everyday lives such as crossing a hallway, passing through a door and intersecting paths on wide open spaces. The proposed cooperative planning method compares favorably against other stat-of-the-art human-aware navigation planning schemes. We have augmented robot navigation behavior with synchronized and responsive movements of the robot head, making the robot look where it is going and occasionally diverting its gaze towards nearby people to acknowledge that robot will avoid any possible collision with them. At any given moment the robot weighs multiple criteria according to the social context and decides where it should turn its gaze. Through an online user study we have shown that such gazing mechanism effectively complements the navigation behavior and it improves legibility of the robot actions. Finally, we have integrated our navigation scheme with a broader supervision system which can jointly generate normative robot behaviors such as approaching a person and adapting the robot speed according to a group of people who the robot guides in airports or museums

Authority-Sharing Control of Assistive Robotic Walkers

A recognized consequence of population aging is a reduced level of mobility, which undermines the life quality of several senior citizens. A promising solution is represented by assisitive robotic walkers, combining the benefits of standard walkers (improved stability and physical support) with sensing and computing ability to guarantee cognitive support. In this context, classical robot control strategies designed for fully autonomous systems (such as fully autonomous vehicles, where the user is excluded from the loop) are clearly not suitable, since the user’s residual abilities must be exploited and practiced. Conversely, to guarantee safety even in the presence of user’s cognitive deficits, the responsibility of controlling the vehicle motion cannot be entirely left to the assisted person. The authority-sharing paradigm, where the control authority, i.e., the capability of controlling the vehicle motion, is shared between the human user and the control system, is a promising solution to this problem. This research develops control strategies for assistive robotic walkers based on authority-sharing: this way, we ensure that the walker provides the user only the help he/she needs for safe navigation. For instance, if the user requires just physical support to reach the restrooms, the robot acts as a standard rollator; however, if the user’s cognitive abilities are limited (e.g., the user does not remember where the restrooms are, or he/she does not recognize obstacles on the path), the robot also drives the user towards the proper corridors, by planning and following a safe path to the restrooms. The authority is allocated on the basis of an error metric, quantifying the distance between the current vehicle heading and the desired movement direction to perform the task. If the user is safely performing the task, he/she is endowed with control authority, so that his/her residual abilities are exploited. Conversely, if the user is not capable of safely solving the task (for instance, he/is going to collide with an obstacle), the robot intervenes by partially or totally taking the control authority to help the user and ensure his/her safety (for instance, avoiding the collision). We provide detailed control design and theoretical and simulative analyses of the proposed strategies. Moreover, extensive experimental validation shows that authority-sharing is a successful approach to guide a senior citizen, providing both comfort and safety. The most promising solutions include the use of haptic systems to suggest the user a proper behavior, and the modification of the perceived physical interaction of the user with the robot to gradually share the control authority using a variable stiffness vehicle handling