Collaborative Autonomy: Human–Robot Interaction to the Test of Intelligent Help (original) (raw)

Towards human-aware cognitive robots

AAAI-06, Stanford …, 2006

Human-robot interaction requires the robot to explicitly reason on human environments and on its own capacities to achieve tasks in a collaborative way with a human partner. We have devised a decisional framework for human-robot interactive task achievement, embedded in a cognitive architecture, and that is aimed to allow the robot not only to accomplish its tasks but also to produce behaviors that support its commitment vis-a-vis its human partner and to interpret human behaviors and intentions. Together and in coherence with this framework, we develop and experiment various task planners and interaction schemes that allow the robot to select and perform its tasks while taking into account explicitly the human abilities as well as the constraints imposed by the presence of humans, their needs and preferences. We present the first results obtained by our "human-aware" task and motion planners and discuss how they can be extended.

Constructing human-robot interaction with standard cognitive architecture

2019

This paper discusses how to extend cognitive models with an explicit interaction model. The work is based on the Standard Model of Cognitive Architecture which is extended by an explicit model for (spoken) interactions following the Constructive Dialogue Modelling (CDM) approach. The goal is to study how to integrate a cognitively appropriate framework into an architecture which allows smooth communication in human-robot interactions, and the starting point is to model construction of shared understanding of the dialogue context and the partner’s intentions. Implementation of conversational interaction is considered important in the context of social robotics which aim to understand and respond to the user’s needs and affective state. The paper describes integration of the architectures but not experimental work towards this goal.

How to use a cognitive architecture for a dynamic person model with a social robot in human collaboration

Workshop Robots for Humans 2024, 2024

The use of cognitive architectures is promising in order to achieve more human-like reactions and behavior in social robots. For example, ACT-R can be used to create a dynamic cognitive person model of a human cooperation partner of the robot. A proof-of-concept for a direct and easy-to-implement integration of ACT-R with the humanoid social robot Pepper is described in this work. An exemplary setup of the system consisting of cognitive architecture and robot application and the type of connection between ACT-R and the robot is explained. Furthermore, an idea is outlined of how the cognitive person model of the human cooperation partner in ACT-R is updated with dynamic data from the real world using the example of emotion recognition by the robot.

What am I? - Complementing a robot's task-solving capabilities with a mental model using a cognitive architecture

8th Workshop on Behavior Adaptation and Learning for Assistive Robotics, 2024

One way to improve Human-Robot Interaction (HRI) and increase trust, acceptance and mutual understanding is to make the behavior of a social robot more comprehensible and understandable for humans. This is particularly important if humans and machines are to work together as partners. To be able to do this, both must have the same basic understanding of the task and the current situation. We created a model within a cognitive architecture connected to the robot. The cognitive model processed relevant conversational data during a dialog with a human to create a mental model of the situation. The dialog parts of the robot were generated with a Large Language Model (LLM) from OpenAI using suitable prompts. An ACT-R model evaluated the data received by the robot according to predefined criteria-in our example application, hierarchical relationships were established and rememberedand provided feedback to the LLM via the application for prompt augmentation with the purpose of adapting or finetuning the request. Initial tests indicated that this approach may have advantages for dialogic tasks and can compensate for weaknesses in terms of a deeper understanding or "blind spots" on the part of the LLM.

Artificial cognition for social human–robot interaction: An implementation

Artificial Intelligence, 2017

Human-Robot Interaction challenges Artificial Intelligence in many regards: dynamic, partially unknown environments that were not originally designed for robots; a broad variety of situations with rich semantics to understand and interpret; physical interactions with humans that requires fine, low-latency yet socially acceptable control strategies; natural and multi-modal communication which mandates common-sense knowledge and the representation of possibly divergent mental models. This article is an attempt to characterise these challenges and to exhibit a set of key decisional issues that need to be addressed for a cognitive robot to successfully share space and tasks with a human. We identify first the needed individual and collaborative cognitive skills: geometric reasoning and situation assessment based on perspective-taking and affordance analysis; acquisition and representation of knowledge models for multiple agents (humans and robots, with their specificities); situated, natural and multi-modal dialogue; human-aware task planning; human-robot joint task achievement. The article discusses each of these abilities, presents working implementations, and shows how they combine in a coherent and original deliberative architecture for human-robot interaction. Supported by experimental results, we eventually show how explicit knowledge management, both symbolic and geometric, proves to be instrumental to richer and more natural human-robot interactions by pushing for pervasive, human-level semantics within the robot's deliberative system.

Investigating Adjustable Social Autonomy in Human Robot Interaction

2021

More and more often, Human Robot Interaction(HRI) applications require the design of robotics systems whose decision process implies the capability to evaluate not only the physical environment, but especially the mental states and the features of its human interlocutor, in order to adapt their social autonomy every time humans require the robot’s help. Robots will be really cooperative and effective when they will expose the capability to consider not only the goals or interests explicitly required by humans, but also those one that are not declared and to provide help that go beyond the literal task execution. In order to improve the quality of this kind of smart help, a robot has to operate a meta-evaluation of its own predictive skills to build a model of the interlocutor and of her/his goals. The robot’s capability to self-trust its skills to interpret the interlocutor and the context, is a fundamental requirement for producing smart and effective decisions towards humans. In t...

Social Human-Robot Interaction: A New Cognitive and Affective Interaction-Oriented Architecture

Lecture Notes in Computer Science, 2016

In this paper, we present CAIO, a Cognitive and Affective Interaction-Oriented architecture for social human-robot interactions (HRI), allowing robots to reason on mental states (including emotions), and to act physically, emotionally and verbally. We also present a short scenario and implementation on a Nao robot. 2 Related works Cognitive architectures have been subject to research for a long time, and good reviews exist (see for example [30, 11]). They mostly fall in three categories: biologically-inspired, philosophically-inspired, and Artificial Intelligence architectures. We illustrate these categories with some of the major and well-known architectures.

A Survey of Behavioral Models for Social Robots

Robotics, 2019

The cooperation between humans and robots is becoming increasingly important in our society. Consequently, there is a growing interest in the development of models that can enhance and enrich the interaction between humans and robots. A key challenge in the Human-Robot Interaction (HRI) field is to provide robots with cognitive and affective capabilities, by developing architectures that let them establish empathetic relationships with users. Over the last several years, multiple models were proposed to face this open-challenge. This work provides a survey of the most relevant attempts/works. In details, it offers an overview of the architectures present in literature focusing on three specific aspects of HRI: the development of adaptive behavioral models, the design of cognitive architectures, and the ability to establish empathy with the user. The research was conducted within two databases: Scopus and Web of Science. Accurate exclusion criteria were applied to screen the 4916 art...

A Cognitive and Affective Architecture for Social Human-Robot Interaction

Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts - HRI'15 Extended Abstracts, 2015

Robots find new applications in our daily life where they interact more and more closely with their human user. Despite a long history of research, existing cognitive architectures are too generic and hence not tailored enough to meet specific needs of social HRI. In particular, interaction-oriented architectures require handling emotions, language, social norms, etc. In this paper, we present an overview of a Cognitive and Affective Interaction-Oriented Architecture for social human-robot interactions, called CAIO. This architecture is in the line of BDI (Belief, Desire, Intention) architecture that comes from action philosophy of Bratman. CAIO integrates complex emotions and planning technics. It aims to contribute to cognitive architectures for HRI by enabling the robot to reason on mental states (including emotions) of the interlocutors, and to act physically, emotionally and verbally.

A Survey of Behavioural Models for Social Robots

2019

The cooperation between humans and robots is becoming increasingly important in our society. Consequently, there is a growing interest in the development of models that can enhance the interaction between humans and robots. A key challenge in the Human-Robot Interaction (HRI) field is to provide robots with cognitive and affective capabilities, developing architectures that let them establish empathetic relationships with users. Several models have been proposed in recent years to solve this open-challenge. This work provides a survey of the most relevant attempts/works. In details, it offers an overview of the architectures present in literature focusing on three specific aspects of HRI: the development of adaptive behavioural models, the design of cognitive architectures, and the ability to establish empathy with the user. The research was conducted within two databases: Scopus and Web of Science. Accurate exclusion criteria were applied to screen the 1007 articles found (at the e...