Short-term human-robot interaction through conditional planning and execution

The deployment of robots in public environments is gaining more and more attention and interest both for the research opportunities and for the possibility of developing commercial applications over it. In these scenarios, proper definitions and implementations of human-robot interactions are crucial and the specific characteristics of the environment (in particular, the presence of untrained users) makes the task of defining and implementing effective interactions particularly challenging. In this paper, we describe a method and a fully implemented robotic system using conditional planning for generating and executing short-term interactions by a robot deployed in a public environment. To this end, the proposed method integrates and extends two components already successfully used for planning in robotics: ROSPlan and Petri Net Plans. The contributions of this paper are the problem definition of generating short-term interactions as a conditional planning problem and the description of a solution fully implemented on a real robot. The proposed method is based on the integration between a contingent planner in ROSPlan and the Petri Net Plans execution framework, and it has been tested in different scenarios where the robot interacted with hundreds of untrained users

Adaptive Process Management in Cyber-Physical Domains

The increasing application of process-oriented approaches in new challenging cyber-physical domains beyond business computing (e.g., personalized healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex processes in such domains. A cyber-physical domain is characterized by the presence of a cyber-physical system coordinating heterogeneous ICT components (PCs, smartphones, sensors, actuators) and involving real world entities (humans, machines, agents, robots, etc.) that perform complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and processes enacted in cyber-physical domains must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this chapter, we tackle the above issue and we propose a general approach, a concrete framework and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events. The adaptation mechanism provided by SmartPM is based on declarative task specifications, execution monitoring for detecting failures and context changes at run-time, and automated planning techniques to self-repair the running process, without requiring to predefine any specific adaptation policy or exception handler at design-time

Neural Task Programming: Learning to Generalize Across Hierarchical Tasks

In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program induction. NTP takes as input a task specification (e.g., video demonstration of a task) and recursively decomposes it into finer sub-task specifications. These specifications are fed to a hierarchical neural program, where bottom-level programs are callable subroutines that interact with the environment. We validate our method in three robot manipulation tasks. NTP achieves strong generalization across sequential tasks that exhibit hierarchal and compositional structures. The experimental results show that NTP learns to generalize well to- wards unseen tasks with increasing lengths, variable topologies, and changing objectives.Comment: ICRA 201

Telerobot task planning and reasoning: Introduction to JPL artificial intelligence research

A view of the capabilities and areas of artificial intelligence research which are required for autonomous space telerobotics extending through the year 2000 is given. In the coming years, JPL will be conducting directed research to achieve these capabilities, as well as drawing heavily on collaborative efforts conducted with other research laboratories

From perception to action and vice versa: a new architecture showing how perception and action can modulate each other simultaneously

Presentado en: 6th European Conference on Mobile Robots (ECMR) Sep 25-27, 2013 Barcelona, SpainArtificial vision systems can not process all the information that they receive from the world in real time because it is highly expensive and inefficient in terms of computational cost. However, inspired by biological perception systems, it is possible to develop an artificial attention model able to select only the relevant part of the scene, as human vision does. From the Automated Planning point of view, a relevant area can be seen as an area where the objects involved in the execution of a plan are located. Thus, the planning system should guide the attention model to track relevant objects. But, at the same time, the perceived objects may constrain or provide new information that could suggest the modification of a current plan. Therefore, a plan that is being executed should be adapted or recomputed taking into account actual information perceived from the world. In this work, we introduce an architecture that creates a symbiosis between the planning and the attention modules of a robotic system, linking visual features with high level behaviours. The architecture is based on the interaction of an oversubscription planner, that produces plans constrained by the information perceived from the vision system, and an object-based attention system, able to focus on the relevant objects of the plan being executed.Spanish MINECO projects TIN2008-06196, TIN2012-38079-C03-03 and TIN2012-38079-C03-02. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec