Alami, Rachid


MuMMER: Socially Intelligent Human-Robot Interaction in Public Spaces

arXiv.org Artificial Intelligence

In the EU-funded MuMMER project, we have developed a social robot designed to interact naturally and flexibly with users in public spaces such as a shopping mall. We present the latest version of the robot system developed during the project. This system encompasses audio-visual sensing, social signal processing, conversational interaction, perspective taking, geometric reasoning, and motion planning. It successfully combines all these components in an overarching framework using the Robot Operating System (ROS) and has been deployed to a shopping mall in Finland interacting with customers. In this paper, we describe the system components, their interplay, and the resulting robot behaviours and scenarios provided at the shopping mall.


Dealing with On-Line Human-Robot Negotiations in Hierarchical Agent-based Task Planner

AAAI Conferences

Collaboration between humans and robots to accomplish different kinds of tasks has been recently studied as a planning problem and several techniques have been developed to define and generate shared plans where humans and robots collaborate to achieve a common goal. However, current methods require the knowledge of the human about the plan under execution and an agreement between users and robots about their roles before the execution of the plan. In this paper, we propose an extension to the Hierarchical Agent-based Task Planner (HATP) that enables humans and robots to negotiate some aspects of the collaboration online during the execution of the plan. The proposed method is based on the automatic generation of a conditional plan in which missing information is acquired at execution time by means of sensing actions. The proposed method has been fully implemented and tested on a real robot performing collaborative tasks in an office-like environment.


Sebastiani

AAAI Conferences

Collaboration between humans and robots to accomplish different kinds of tasks has been recently studied as a planning problem and several techniques have been developed to define and generate shared plans where humans and robots collaborate to achieve a common goal. However, current methods require the knowledge of the human about the plan under execution and an agreement between users and robots about their roles before the execution of the plan. In this paper, we propose an extension to the Hierarchical Agent-based Task Planner (HATP) that enables humans and robots to negotiate some aspects of the collaboration online during the execution of the plan. The proposed method is based on the automatic generation of a conditional plan in which missing information is acquired at execution time by means of sensing actions. The proposed method has been fully implemented and tested on a real robot performing collaborative tasks in an office-like environment.


A Novel Software Combining Task and Motion Planning for Human-Robot Interaction

AAAI Conferences

Task planning and motion planning softwares operate on very different representations, making it hard to link them. We propose a software filling that gap in a generic way, mainly by choosing the best way to physically perform a task according to a higher level plan and taking explicitly into account human comfort and preferences.


Waldhart

AAAI Conferences

Task planning and motion planning softwares operate on very different representations, making it hard to link them. We propose a software filling that gap in a generic way, mainly by choosing the best way to physically perform a task according to a higher level plan and taking explicitly into account human comfort and preferences.


A Few AI Challenges Raised while Developing an Architecture for Human-Robot Cooperative Task Achievement

AAAI Conferences

Over the last five years, and while developing an architecture for autonomous service robots in human environments, we have identified several key decisional issues that are to be tackled for a cognitive robot to share space and tasks with a human. We introduce some of them here: situation assessment and mutual modelling, management and exploitation of each agent (human and robot) knowledge in separate cognitive models, natural multi-modal communication, "human-aware" task planning, and human and robot interleaved plan achievement. As a general "take home" message, it appears that explicit knowledge management, both symbolic and geometric, proves to be a successful key while attempting to address these challenges, as it pushes for a different, more semantic way to address the decision-making issue in human-robot interactions.


Lemaignan

AAAI Conferences

Over the last five years, and while developing an architecture for autonomous service robots in human environments, we have identified several key decisional issues that are to be tackled for a cognitive robot to share space and tasks with a human. We introduce some of them here: situation assessment and mutual modelling, management and exploitation of each agent (human and robot) knowledge in separate cognitive models, natural multi-modal communication, "human-aware" task planning, and human and robot interleaved plan achievement. As a general "take home" message, it appears that explicit knowledge management, both symbolic and geometric, proves to be a successful key while attempting to address these challenges, as it pushes for a different, more semantic way to address the decision-making issue in human-robot interactions.


HATP: An HTN Planner for Robotics

arXiv.org Artificial Intelligence

Hierarchical Task Network (HTN) planning is a popular approach that cuts down on the classical planning search space by relying on a given hierarchical library of domain control knowledge. This provides an intuitive methodology for specifying high-level instructions on how robots and agents should perform tasks, while also giving the planner enough flexibility to choose the lower-level steps and their ordering. In this paper we present the HATP (Hierarchical Agent-based Task Planner) planning framework which extends the traditional HTN planning domain representation and semantics by making them more suitable for roboticists, and treating agents as "first class" entities in the language. The former is achieved by allowing "social rules" to be defined which specify what behaviour is acceptable/unacceptable by the agents/robots in the domain, and interleaving planning with geometric reasoning in order to validate online -with respect to a detailed geometric 3D world- the human/robot actions currently being pursued by HATP.


Towards Combining HTN Planning and Geometric Task Planning

arXiv.org Artificial Intelligence

In this paper we present an interface between a symbolic planner and a geometric task planner, which is different to a standard trajectory planner in that the former is able to perform geometric reasoning on abstract entities---tasks. We believe that this approach facilitates a more principled interface to symbolic planning, while also leaving more room for the geometric planner to make independent decisions. We show how the two planners could be interfaced, and how their planning and backtracking could be interleaved. We also provide insights for a methodology for using the combined system, and experimental results to use as a benchmark with future extensions to both the combined system, as well as to the geometric task planner.


Visuo-Spatial Ability, Effort and Affordance Analyses: Towards Building Blocks for Robot's Complex Socio-Cognitive Behaviors

AAAI Conferences

For the long term co-existence of robots with us in complete harmony, they will be expected to show sociocognitive behaviors. In this paper, taking inspiration from child development research and human behavioral psychology we will identify the basic but key capabilities: perceiving abilities, effort and affordances. Further we will present the concepts, which fuse these components to perform multi-effort ability and affordance analysis. We will show instantiations of these capabilities on real robot and will discuss its potential applications for more complex socio-cognitive behavior.