A Socially Adaptable Framework for Human-Robot Interaction

  title={A Socially Adaptable Framework for Human-Robot Interaction},
  author={Ana Tanevska and Francesco Rea and Giulio Sandini and Lola Ca{\~n}amero and Alessandra Sciutti},
  journal={Frontiers in Robotics and AI},
In our everyday lives we regularly engage in complex, personalized, and adaptive interactions with our peers. To recreate the same kind of rich, human-like interactions, a social robot should be aware of our needs and affective states and continuously adapt its behavior to them. Our proposed solution is to have the robot learn how to select the behaviors that would maximize the pleasantness of the interaction for its peers. To make the robot autonomous in its decision making, this process could… 
Exploring Human attitude during Human-Robot Interaction
The outcome of this work can be integrated into a robotic platform to automatically assess the quality of interaction and to modify its behavior accordingly.
Affect-Aware Learning for Social Robots
This work proposes a recently developed scenario, based on a competitive game, as a tool to steer the development of socially-aware competitive reinforcement learning (RL) and suggests that even basic cognitive processes could benefit from considering the social and affective dimensions.
General Framework for the Optimization of the Human-Robot Collaboration Decision-Making Process Through the Ability to Change Performance Metrics
The decision-making process is based on Nash Equilibrium and perfect-information extensive form from game theory and can deal with collaborative interactions considering different performance metrics such as optimizing the time to complete the task, considering the probability of human errors, etc.
The experience of older persons with mental health conditions who interact with healthcare robots and nurse intermediaries: The qualitative case studies
Background: Caring expressions between humans and nonhuman intelligent machines are futuristic prototypes with healthcare robots as major advocates. Objective: To examine the experience of older
You Were Always on My Mind: Introducing Chef’s Hat and COPPER for Personalized Reinforcement Learning
The Chef’s Hat simulation environment is proposed, which implements a multi-agent competitive card game that is a complete reproduction of the homonymous board game, designed to provoke competitive strategies in humans and emotional responses and helps agents learn to adapt to different types of opponents, improving the performance when compared to off-line learning models.
PhD Program in Bioengineering and Robotics
1 ENABLING ROBOTS TO UNDERSTAND AND ADAPT TO HUMANS ....................................................................... 4 2 ASSESSING AND TRAINING VISUO-HAPTIC EXPLORATION


A Cognitive Architecture for Socially Adaptable Robots
This work proposes a cognitive architecture for the humanoid robot iCub supporting adaptability and attempts to validate its functionality and establish the potential benefits it could bring with respect to the more traditional pre-scripted interaction protocols for robots.
Eager to Learn vs. Quick to Complain? How a socially adaptive robot architecture performs with different robot personalities
This work proposes a cognitive architecture for the humanoid robot iCub supporting adaptability and attempts to validate its functionality and test different robot profiles.
Designing an Affective Cognitive Architecture for Human-Humanoid Interaction
This work proposes to develop an affect-based architecture for the humanoid robot iCub with the purpose of fully autonomous personalized HRI, which can be generalized to fit many different contexts -social, educational, collaborative and assistive - allowing for natural, long-term, and adaptive interaction.
Artificial cognition for social human-robot interaction: An implementation
It is shown how explicit knowledge management, both symbolic and geometric, proves to be instrumental to richer and more natural humanrobot interactions by pushing for pervasive, human-level semantics within the robot's deliberative system.
Eliciting caregiving behavior in dyadic human-robot attachment-like interactions
The architecture and setup of an arousal-based model controlling the behavior of a Sony AIBO robot during the exploration of a novel environment: a children's play mat succeeded in eliciting positive and caregiving behavior from adults of different age groups and technological background.
Attachment bonds for Human-like Robots
A Perception–Action architecture and experiments to simulate imprinting — the establishment of strong attachment links with a "caregiver" — in a robot are presented, which do not consider imprinting as rigidly timed and irreversible, but as a more flexible phenomenon that allows for further adaptation as a result of reward-based learning through experience.
Arousal regulation and affective adaptation to human responsiveness by a robot that explores and learns a novel environment
This paper investigates how a ‘baby’ robot that explores and learns novel environments can adapt its affective regulatory behavior of soliciting help from a “caregiver” to the preferences shown by the caregiver in terms of varying responsiveness.
How to build robots that make friends and influence people
  • C. Breazeal, B. Scassellati
  • Psychology, Computer Science
    Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289)
  • 1999
This work constructs a robot which exploits natural human social tendencies to convey intentionality through motor actions and facial expressions and presents results on the integration of perception, attention, motivation, behavior, and motor systems which allow the robot to engage in infant-like interactions with a human caregiver.
A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI
This survey paper presents an encompassing review of existing automated affect recognition and classification systems for social robots engaged in various HRI settings and discusses pertinent future research directions for promoting the development of socially intelligent robots capable of recognizing, classifying and responding to human affective states during real-time HRI.
Toward a framework for levels of robot autonomy in human-robot interaction
This framework proposes a process for determining a robot's autonomy level by categorizing autonomy along a 10-point taxonomy and considering HRI variables (e.g., acceptance, situation awareness, reliability) that may be influenced by the LORA.