Human Robot Interaction (HRI) is an active field of integrating and embedding different techniques in artificial intelligence. This paper describes my research topic on: Control of Robotic Systems for Safe Interaction with Human Operators. It consists of online motion generation for robotic manipulators interacting with dynamic obstacles and humans using a moving horizon scheme, modeling and long term prediction of human motion using probabilistic models and reachability analysis, and development of an HRI demonstration platform.

Deep space missions are characterized by severely constrained communication links. To meet the needs of future missions and increase their scientific return, future space systems will require an increased level of autonomy on-board. In this work, we propose a comprehensive approach to on-board autonomy relying on model-based reasoning, and encompassing many important reasoning capabilities such as plan generation, validation, execution and monitoring, FDIR, and run-time diagnosis. The controlled platform is represented symbolically, and the reasoning capabilities are seen as symbolic manipulation of such formal model. We have developed a prototype of our framework, implemented within an on-board Autonomous Reasoning Engine. We have evaluated our approach on two case-studies inspired by real-world, ongoing projects, and characterized it in terms of reliability, availability and performance.

A* with admissible heuristics is a very successful approach to optimal planning. But how to derive such heuristics automatically? Merge-and-shrink abstraction (M&S) is a general approach to heuristic design whose key advantage is its capability to make very fine-grained choices in defining abstractions. However, little is known about how to actually make these choices. We address this via the well-known notion of bisimulation. When aggregating only bisimilar states, M&S yields a perfect heuristic. Alas, bisimulations are exponentially large even in trivial domains. We show how to apply label reduction — not distinguishing between certain groups of operators — without incurring any information loss, while potentially reducing bisimulation size exponentially. In several benchmark domains, the resulting algorithm computes perfect heuristics in polynomial time. Empirically, we show that approximating variants of this algorithm improve the state of the art in M&S heuristics. In particular, a simple hybrid of two such variants is competitive with the leading heuristic LM-cut.

We propose a novel online planning algorithm for ad hoc team settings — challenging situations in which an agent must collaborate with unknown teammates without prior coordination. Our approach is based on constructing and solving a series of stage games, and then using biased adaptive play to choose actions. The utility function in each stage game is estimated via Monte-Carlo tree search using the UCT algorithm. We establish analytically the convergence of the algorithm and show that it performs well in a variety of ad hoc team domains.

Planning with partial observability can be formulated as a non-deterministic search problem in belief space. The problem is harder than classical planning as keeping track of beliefs is harder than keeping track of states, and searching for action policies is harder than searching for action sequences. In this work, we develop a framework for partial observability that avoids these limitations and leads to a planner that scales up to larger problems. For this, the class of problems is restricted to those in which 1) the non-unary clauses representing the uncertainty about the initial situation are nvariant, and 2) variables that are hidden in the initial situation do not appear in the body of conditional effects, which are all assumed to be deterministic. We show that such problems can be translated in linear time into equivalent fully observable non-deterministic planning problems, and that an slight extension of this translation renders the problem solvable by means of classical planners. The whole approach is sound and complete provided that in addition, the state-space is connected. Experiments are also reported.

Microblogging websites such as Twitter are increasingly being used by businesses/campaigners for timely dissemination of information to their followers. The diffusion of a tweet depends on several factors: the activity of the follower nodes, the responsiveness of follower nodes to tweets from the source node, the out-degree of the follower nodes, the content of recent related tweets seen by the follower node, etc. Using such factors, in this paper, we propose a framework to measure the effectiveness of an information campaign over Twitter. We consider a positive as well as a negative metric to measure the impact of a tweet: while retweets are used to measure the positive impact, the lack of a timely response from an active follower node is taken as a potential negative impact. We investigate the scheduling of tweets to increase the net positive impact while keeping the net negative impact below a desired level. We propose and study several scheduling algorithms by casting the problem in a Markov Decision Process (MDP) framework. In order to compare our algorithms, we estimate the model parameters from tweet data collected using the Twitter API from an arbitrarily selected node and its 6837 followers over several months. For this dataset, we find that if successive tweets in the campaign are novel, then substantial gains over user activity based scheduling can be obtained by scheduling tweets in time slots where the ratio of the expected positive and negative metrics is high. We call this the MaxRatio policy and we show that it is optimal under certain conditions. In cases where we are not certain about the response of users to successive related tweets, we identify another algorithm (which we call MaxReach) as a robust alternative.

In this article, we discuss the problem of transferring search heuristics from one planner to another. More specifically, we demonstrate how to transfer the domain-dependent heuristics acquired by one planner into a second planner. Our motivation is to improve the efficiency and the efficacy of the second planner by allowing it to use the transferred heuristics to capture domain regularities that it would not otherwise recognize. Our experimental results show that the transferred knowledge does improve the second planner's performance on novel tasks over a set of seven benchmark planning domains.

In this article, we discuss the problem of transferring search heuristics from one planner to another. More specifically, we demonstrate how to transfer the domain-dependent heuristics acquired by one planner into a second planner. Our motivation is to improve the efficiency and the efficacy of the second planner by allowing it to use the transferred heuristics to capture domain regularities that it would not otherwise recognize. Our experimental results show that the transferred knowledge does improve the second planner's performance on novel tasks over a set of seven benchmark planning domains.

In a number of graph search-based planning problems, the value of the cost function that is being minimized also affects the set of possible actions at some or all the states in the graph. In such planning problems, the cost function typically becomes one of the state variables thereby increasing the dimensionality of the planning problem, and consequently the size of the graph that represents the problem. In this paper, we show how to avoid this increase in the dimensionality for weighted search (with bounded suboptimality) whenever the availability of the actions is monotonically non-increasing with the increase in the cost function.

Rapidly-exploring random trees (RRTs) are data structures and search algorithms designed to be used in continuous path planning problems. They are one of the most successful state-of-the-art techniques as they offer a great degree of flexibility and reliability. However, their use in other search domains has not been thoroughly analyzed. In this work we propose the use of RRTs as a search algorithm for automated planning. We analyze the advantages that this approach has over previously used search algorithms and the challenges of adapting RRTs for implicit and discrete search spaces.