We focus on a rich axiomatization for actions in the situation calculus that includes, among other features, a solution to the frame problem for deterministic actions. Our work is foundational in nature, directed at simplifying the entailment problem for these axioms. Specifically, we make four contributions to the metatheory of situation calculus axiomatizations of dynamical systems:(1) We prove that the above-mentioned axiomatization for actions has a relative satisfiability property; the full axiomatization is satisfiable iff the axioms for the initial state are.(2) We define the concept of regression relative to these axioms, and prove a soundness and completeness theorem for a regression-based approach to the entailment problem for a wide class of queries.(3) Our formalization of the situation calculus requires certain foundational axioms specifying the domain of situations. These include an induction axiom, whose presence complicates human and automated reasoning in the situation calculus. We characterize various classes of sentences whose proofs do not require induction, and in some cases, some of the other foundational axioms.(4) We prove that the logic programming language GOLOG never requires any of the foundational axioms for the evaluation of programs.

-In May 2012, two major earthquakes occurred in the Emilia-Romagna region, Northern Italy, followed by further aftershocks and earthquakes in June 2012. This sequence of earthquakes and shocks caused multiple casualties, and widespread damage to numerous historical buildings in the region. The Italian National Fire Corps deployed disaster response and recovery of people and buildings. In June 2012, they requested the aid of the EU-funded project NIFTi, to assess damage to historical buildings, and cultural artifacts located therein. To this end, NIFTi deployed a team of humans and robots (UGV, UAV) in the red-area of Mirandola, EmiliaRomagna, from Tuesday July 24 until Friday July 27, 2012. The team worked closely together with the members of the Italian National Fire Corps involved in the red area. This paper describes the deployment, and experience.

The paper describes experience with applying a user-centric design methodology in developing systems for human-robot teaming in Urban Search and Rescue. A human-robot team consists of several semi-autonomous robots (rovers/UGVs, microcopter/UAVs), several humans at an off-site command post (mission commander, UGV operators) and one on-site human (UAV operator). This system has been developed in close cooperation with several rescue organizations, and has been deployed in a real-life tunnel accident use case. The human-robot team jointly explores an accident site, communicating using a multi-modal team interface, and spoken dialogue. The paper describes the development of this complex socio-technical system per se, as well as recent experience in evaluating the performance of this system

We introduce a 3D human pose estimation method from single image, based on a hierarchical Bayesian non-parametric model. The proposed model relies on a representation of the idiosyncratic motion of human body parts, which is captured by a subdivision of the human skeleton joints into groups. A dictionary of motion snapshots for each group is generated. The hierarchy ensures to integrate the visual features within the pose dictionary. Given a query image, the learned dictionary is used to estimate the likelihood of the group pose based on its visual features. The full-body pose is reconstructed taking into account the consistency of the connected group poses. The results show that the proposed approach is able to accurately reconstruct the 3D pose of previously unseen subjects.

This paper describes our experience in designing, developing and deploying systems for supporting human-robot teams during disaster response. It is based on R&D performed in the EU-funded project NIFTi. NIFTi aimed at building intelligent, collaborative robots that could work together with humans in exploring a disaster site, to make a situational assessment. To achieve this aim, NIFTi addressed key scientific design aspects in building up situation awareness in a human-robot team, developing systems using a user-centric methodology involving end users throughout the entire R&D cycle, and regularly deploying implemented systems under real-life circumstances for experimentation and testing. This has yielded substantial scientific advances in the state-of-the-art in robot mapping, robot autonomy for operating in harsh terrain, collaborative planning, and human-robot interaction. NIFTi deployed its system in actual disaster response activities in Northern Italy, in July 2012, aiding in structure damage assessment.

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This paper contains a description of the project TRADR: Long-Term Human-Robot Teaming for Robot Assisted Disaster Response. As robotic disaster relief systems are still scarce, any incident serious enough to render robot involvement will most likely involve a sequence of sorties over several hours, days and even months. TRADR focuses on the persistence of environment models, multi-robot action models, and human-robot teaming, in order to allow incremental capability improvement over the duration of a mission. TRADR applies a user centric design approach to disaster response robotics, with use cases involving the response to a medium to large scale industrial accident by teams consisting of human rescuers and several robots (both ground and airborne). The paper overviews the project objectives and motivation, the structure and approach, as well as the partners and related work.Keywords disaster response robotics · persistent environment models · persistent multi-robot action models · persistent human-robot teaming · user-centric design

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