The work presented here is a culmination of developments within the Swedish project COIN: Co-adaptive human-robot interactive systems, funded by the Swedish Foundation for Strategic Research (SSF), which addresses a unified framework for co-adaptive methodologies in human-robot co-existence. We investigate co-adaptation in the context of safe planning/control, trust, and multi-modal human-robot interactions, and present novel methods that allow humans and robots to adapt to one another and discuss directions for future work.

In this paper, we propose a ROS software package for planning and control of robotic systems with a human-in-the-Ioop focus. The software uses temporal logic specifications, specifically Linear Temporal Logic, for a language-based method to develop correct-by-design high level robot plans. The approach is structured to allow a human to adjust the high-level plan online. A human may also take control of the robot (in a low-level control fashion), but the software prevents the human from implementing dangerous behaviour that would violate the high-level task specification. Finally, the planner is able to learn human-preferred high-level tasks by tracking human low-level control inputs in an inverse learning framework. The proposed approach is demonstrated in a warehouse setting with multiple robot agents to showcase the efficacy of the proposed solution.

In this paper we consider a mobile platform controlled by two entities; an autonomousagent and a human user. The human aims for the mobile platform to complete a task, whichwe will denote as the human task, and will impose a control input accordingly, while not beingaware of any other tasks the system should or must execute. The autonomous agent will in turnplan its control input taking in consideration all safety requirements which must be met, sometask which should be completed as much as possible (denoted as the robot task), as well aswhat it believes the human task is based on previous human control input. A framework for theautonomous agent and a mixed initiative controller are designed to guarantee the satisfaction ofthe safety requirements while both the human and robot tasks are violated as little as possible.The framework includes an estimation algorithm of the human task which will improve witheach cycle, eventually converging to a task which is similar to the actual human task. Hence, theautonomous agent will eventually be able to find the optimal plan considering all tasks and thehuman will have no need to interfere again. The process is illustrated with a simulated example

In this paper we present a control synthesis framework for a multi-agent system under hard and soft constraints, which performs online re-planning to achieve collision avoidance and execution of the optimal path with respect to some human preference considering the type of the violation of the soft constraints. The human preference is indicated by a mixed initiative controller and the resulting change of trajectory is used by an inverse reinforcement learning based algorithm to improve the path which the affected agent tries to follow. A case study is presented to validate the result.

With the increase of robotic presence in our homes and work environment, it has become imperative to consider human-in-the-loop systems when designing robotic controllers. This includes both a physical presence of humans as well as interaction on a decision and control level. One important aspect of this is to design controllers which are guaranteed to satisfy specified safety constraints. At the same time we must minimize the risk of not finding solutions, which would force the system to stop. This require some room for relaxation to be put on the specifications. Another aspect is to design the system to be adaptive to the human and its environment.

In this thesis we approach the problem by considering control synthesis for multi-agent systems under hard and soft constraints, where the human has direct impact on how the soft constraint is violated. To handle the multi-agent structure we consider both a classical centralized automata based framework and a decentralized approach with collision avoidance. To handle soft constraints we introduce a novel metric; hybrid distance, which quantify the violation. The hybrid distance consists of two types of violation; continuous distance or missing deadlines, and discrete distance or spacial violation. These distances are weighed against each other with a weight constant we will denote as the human preference constant. For the human impact we consider two types of feedback; direct feedback on the violation in the form of determining the human preference constant, and direct control input through mixed-initiative control where the human preference constant is determined through an inverse reinforcement learning algorithm based on the suggested and followed paths. The methods are validated through simulations.

I takt med att robotar blir allt vanligare i våra hem och i våra arbetsmiljöer, har det blivit allt viktigare att ta hänsyn till människan plats i systemen när regulatorerna för robotorna designas. Detta innefattar både människans fysiska närvaro och interaktion på besluts- och reglernivå. En viktig aspekt i detta är att designa regulatorer som garanterat uppfyller givna villkor. Samtidigt måste vi minimera risken att ingen lösning hittas, eftersom det skulle tvinga systemet till ett stopp. För att uppnå detta krävs det att det finns rum för att mjuka upp villkoren. En annan aspekt är att designa systemet så att det är anpassningsbart till människan och miljön.

I den här uppsatsen närmar vi oss problemet genom att använda regulator syntes för multi-agent system under hårda och mjuka villkor där människan har direkt påverkan på hur det svaga villkoret överträds. För att hantera multi-agent strukturen undersöker vi både det klassiska centraliserade automata-baserade ramverket och ett icke-centraliserat tillvägagångsätt med krockundvikning. För att hantera mjuka villkor introducerar vi en metrik; hybrida avståndet, som kvantifierar överträdelsen. Det hybrida avståndet består av två typer av överträdelse (kontinuerligt avstånd eller missandet av deadlines, och diskret avstånd eller rumsliga överträdelser) som vägs mot varandra med en vikt konstant som vi kommer att kalla den mänskliga preferens kontanten. Som mänsklig påverkan överväger vi direkt feedback på överträdelsen genom att hon bestämmer värdet på den mänskliga preferens kontanten, och direkt påverkan på regulatorn där den mänskliga preferens konstanten bestäms genom en inverserad förstärkt inlärnings algoritm baserad på de föreslagna och följda vägarna. Metoderna valideras genom simuleringar.

Recently, multiple frameworks for control synthesis under temporal logic have been suggested. The frameworks allow a user to give one or a set of robots high level tasks of different properties (e.g. temporal, time limited, individual and cooperative). However, the issue of how to handle tasks, which either seem to be or are infeasible, remains unsolved. In this paper we introduce a human to the loop, using the human's feedback to determine preference towards different types of violations of the tasks. We introduce a metric of violation called hybrid distance. We also suggest a novel framework for synthesizing a least violating controller with respect to the hybrid distance and the human feedback. Simulation result indicate that the suggested framework gives reasonable estimates of the metric, and that the suggested plans correspond to the expected ones.

This paper considers the motion control and task planning problem of mobile robots under complex high-level tasks and human initiatives. The assigned task is specified as Linear Temporal Logic (LTL) formulas that consist of hard and soft constraints. The human initiative influences the robot autonomy in two explicit ways: with additive terms in the continuous controller and with contingent task assignments. We propose an online coordination scheme that encapsulates (i) a mixed-initiative continuous controller that ensures all-time safety despite of possible human errors, (ii) a plan adaptation scheme that accommodates new features discovered in the workspace and short-term tasks assigned by the operator during run time, and (iii) an iterative inverse reinforcement learning (IRL) algorithm that allows the robot to asymptotically learn the human preference on the parameters during the plan synthesis. The results are demonstrated by both realistic human-in-the-loop simulations and experiments.

This paper presents a framework for automatic synthesis of a control sequence for multi-agent systems governed by continuous linear dynamics under timed constraints. First, the motion of the agents in the workspace is abstracted into individual Transition Systems (TS). Second, each agent is assigned with an individual formula given in Metric Interval Temporal Logic (MITL) and in parallel, the team of agents is assigned with a collaborative team formula. The proposed method is based on a correct-by-construction control synthesis method, and hence guarantees that the resulting closed-loop system will satisfy the desired specifications. The specifications considers boolean-valued properties under real-time bounds. Extended simulations has been performed in order to demonstrate the efficiency of the proposed methodology.

The problem of synthesizing controllers for motion planning of multi-agent systems under Linear Temporal Logic (LTL) high-level specifications has been of great interest and has been widely studied over the last years. However, LTL cannot handle time constraints as specifications. The time aspect would allow more complicated and specific tasks and it is therefore desirable to incorporate. This work aims to determine how control synthesis for a continuous linear system can be performed based on Metric Interval Temporal Logic (MITL), which is able to handle desired time constraints to high-level specifications. Firstly, a control design synthesis method for a single-agent, based on previous work within both the field of LTL and MITL is presented. Secondly, a control design synthesis method for multi-agent systems considering both local an global MITL specifications is presented. Extended simulations has been performed in MATLAB environment demonstrating the two proposed methodologies. The result shows that the methods guarantee that the MITL specifications are satisfied, for all cases for which a solution is found.

Problemet gällande regulator syntetisering for rörelse planering av fler-agents system under Line-ar Temporal Logic (Linjär Temporal Logik=LTL) hög-nivå specifikationer har varit av stort intresse och har studerats brett under de senaste åren. LTL kan emellertid inte hantera tidsbegränsningar som specifikationer. Tidsaspekten skulle tillåta mer komplicerade och specifika uppgifter. Det är därför önskvärt att inkorporera. Målet med det här arbetet är att fastställa hur regulator syntetisering för ett kontinuerligt, linjärt system kan utföras utgående från Metric Interval Temporal Logic (Metrisk Intervall Temporal Logic =MITL), en gren av Temporal Logik som kan hantera de önskvärda tidsbegränsningarna för högnivå specifikationer. Först presenteras en metod för att syntetisera regulatorer för en-agents system. Metoden är baserad på tidigare arbeten inom fälten LTL och MITL. Sedan presenteras en metod för att syntetisera regulatorer för fler-agents system som ¨önskas uppfylla såväl lokala som globala MITL specifikationer. Utbredda simulationer har genomförts i MATLAB miljö för att demonstrera de två˚ föreslagna metoderna. Resultatet visar att metoderna garanterar att MITL specifikationerna är uppfyllda för alla fall för vilka en lösning hittas.