Trust-Based Control of Robotic Manipulators in Collaborative Assembly in Manufacturing

Human-robot interaction (HRI) is vastly addressed in the field of automation and manufacturing. Most of the HRI literature in manufacturing explored physical human-robot interaction (pHRI) and invested in finding means for ensuring safety and optimized effort sharing amongst a team of humans and robots. The recent emergence of safe, lightweight, and human-friendly robots has opened a new realm for human-robot collaboration (HRC) in collaborative manufacturing. For such robots with the new HRI functionalities to interact closely and effectively with a human coworker, new human-centered controllers that integrate both physical and social interaction are demanded. Social human-robot interaction (sHRI) has been demonstrated in robots with affective abilities in education, social services, health care, and entertainment. Nonetheless, sHRI should not be limited only to those areas. In particular, we focus on human trust in robot as a basis of social interaction. Human trust in robot and robot anthropomorphic features have high impacts on sHRI. Trust is one of the key factors in sHRI and a prerequisite for effective HRC. Trust characterizes the reliance and tendency of human in using robots. Factors within a robotic system (e.g. performance, reliability, or attribute), the task, and the surrounding environment can all impact the trust dynamically. Over-reliance or under-reliance might occur due to improper trust, which results in poor team collaboration, and hence higher task load and lower overall task performance. The goal of this dissertation is to develop intelligent control algorithms for the manipulator robots that integrate both physical and social HRI factors in the collaborative manufacturing. First, the evolution of human trust in a collaborative robot model is identified and verified through a series of human-in-the-loop experiments. This model serves as a computational trust model estimating an objective criterion for the evolution of human trust in robot rather than estimating an individual\u27s actual level of trust. Second, an HRI-based framework is developed for controlling the speed of a robot performing pick and place tasks. The impact of the consideration of the different level of interaction in the robot controller on the overall efficiency and HRI criteria such as human perceived workload and trust and robot usability is studied using a series of human-in-the-loop experiments. Third, an HRI-based framework is developed for planning and controlling the robot motion in performing hand-over tasks to the human. Again, series of human-in-the-loop experimental studies are conducted to evaluate the impact of implementation of the frameworks on overall efficiency and HRI criteria such as human workload and trust and robot usability. Finally, another framework is proposed for the cooperative manipulation of a common object by a team of a human and a robot. This framework proposes a trust-based role allocation strategy for adjusting the proactive behavior of the robot performing a cooperative manipulation task in HRC scenarios. For the mentioned frameworks, the results of the experiments show that integrating HRI in the robot controller leads to a lower human workload while it maintains a threshold level of human trust in robot and does not degrade robot usability and efficiency

FABRIC: A Framework for the Design and Evaluation of Collaborative Robots with Extended Human Adaptation

A limitation for collaborative robots (cobots) is their lack of ability to adapt to human partners, who typically exhibit an immense diversity of behaviors. We present an autonomous framework as a cobot's real-time decision-making mechanism to anticipate a variety of human characteristics and behaviors, including human errors, toward a personalized collaboration. Our framework handles such behaviors in two levels: 1) short-term human behaviors are adapted through our novel Anticipatory Partially Observable Markov Decision Process (A-POMDP) models, covering a human's changing intent (motivation), availability, and capability; 2) long-term changing human characteristics are adapted by our novel Adaptive Bayesian Policy Selection (ABPS) mechanism that selects a short-term decision model, e.g., an A-POMDP, according to an estimate of a human's workplace characteristics, such as her expertise and collaboration preferences. To design and evaluate our framework over a diversity of human behaviors, we propose a pipeline where we first train and rigorously test the framework in simulation over novel human models. Then, we deploy and evaluate it on our novel physical experiment setup that induces cognitive load on humans to observe their dynamic behaviors, including their mistakes, and their changing characteristics such as their expertise. We conduct user studies and show that our framework effectively collaborates non-stop for hours and adapts to various changing human behaviors and characteristics in real-time. That increases the efficiency and naturalness of the collaboration with a higher perceived collaboration, positive teammate traits, and human trust. We believe that such an extended human adaptation is key to the long-term use of cobots.Comment: The article is in review for publication in International Journal of Robotics Researc

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

Design of an UAV swarm

This master thesis tries to give an overview on the general aspects involved in the design of an UAV swarm. UAV swarms are continuoulsy gaining popularity amongst researchers and UAV manufacturers, since they allow greater success rates in task accomplishing with reduced times. Appart from this, multiple UAVs cooperating between them opens a new field of missions that can only be carried in this way. All the topics explained within this master thesis will explain all the agents involved in the design of an UAV swarm, from the communication protocols between them, navigation and trajectory analysis and task allocation

Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning

Developing a safe and efficient collision avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generate its paths without observing other robots' states and intents. While other distributed multi-robot collision avoidance systems exist, they often require extracting agent-level features to plan a local collision-free action, which can be computationally prohibitive and not robust. More importantly, in practice the performance of these methods are much lower than their centralized counterparts. We present a decentralized sensor-level collision avoidance policy for multi-robot systems, which directly maps raw sensor measurements to an agent's steering commands in terms of movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to find an optimal policy which is trained over a large number of robots on rich, complex environments simultaneously using a policy gradient based reinforcement learning algorithm. We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system. We also demonstrate that the learned policy can be well generalized to new scenarios that do not appear in the entire training period, including navigating a heterogeneous group of robots and a large-scale scenario with 100 robots. Videos are available at https://sites.google.com/view/drlmac