Robotics Thesis Defense

  • Remote Access Enabled - Zoom
  • Virtual Presentation
  • Ph.D. Student
  • Robotics Institute
  • Carnegie Mellon University
Thesis Orals

Automated Action Selection and Embodied Simulation for Socially Assistive Robots using Standardized Interactions

Robots have the tremendous potential of assisting people in their lives, allowing them to achieve goals that they would not be able to achieve by themselves. In particular, socially assistive robots provide assistance primarily through social interaction, in healthcare, therapy, and education contexts. Despite their potential, current socially assistive robots still lack robust interactive capabilities to allow them to carry out assistive tasks flexibly and autonomously. Some challenges for these robots include responding to and engaging in multi-modal behavior, operating with minimal expert intervention, and accommodating different user needs.

Motivated by these challenges, this thesis aims at augmenting the algorithmic capabilities of such robots by leveraging the structure of existing standardized human-human interactions in assistive domains. Using therapy for Autism Spectrum Disorder (ASD) as a domain of focus, we explore two roles for a socially assistive robot: ‘provider’ and ‘receiver’.

In the provider role, the robot proactively engages in assistive tasks with a human receiver (namely a child with ASD), following standardized interactive tasks. We contribute a family of algorithms for automated action selection, whose goal is to build cost-optimal robot action sequences that account for a range of receiver profiles. We further estimate the action parameters needed to run these algorithms through empirical studies with children with ASD and psychology experts, and show that the algorithms are able to generate personalized action sequences according to different child profiles.

In the receiver role, the robot simulates common behavioral responses of children with ASD to the standardized actions, acting as an aid for providers in training. By reversing the diagnosis pipeline, we first develop a simulation method that generates behaviors consistent with user-controllable receiver profiles. In a second step, we develop an interactive robot capable of responding to a therapist’s actions in an embodied fashion. Our evaluation studies conducted with therapists validate the designed robot behaviors and show promising results for the integration of such robots in clinical training.

These contributions allow for a richer set of interactions with robots in assistive contexts, and are expected to increase their autonomy, flexibility, and effectiveness when dealing with diverse user populations.

Thesis Committee:
Manuela Veloso (Co-Chair)
Francisco S. Melo (Co-Chair) (NESC-ID/Technical University of Lisbon)
Henny Admoni
Aaron Steinfeld
Luca Iocchi (Sapienza University of Rome)
Iolanda Leite (KTH Royal Institute of Technology)

Additional Thesis Information

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