Automating the generation of Planning Domain Definition Language (PDDL) with Large Language Model (LLM) opens new research topic in AI planning, particularly for complex real-world tasks. This paper introduces Image2PDDL, a novel framework that leverages Vision-Language Models (VLMs) to automatically convert images of initial states and descriptions of goal states into PDDL problems. By providing a PDDL domain alongside visual inputs, Imasge2PDDL addresses key challenges in bridging perceptual understanding with symbolic planning, reducing the expertise required to create structured problem instances, and improving scalability across tasks of varying complexity. We evaluate the framework on various domains, including standard planning domains like blocksworld and sliding tile puzzles, using datasets with multiple difficulty levels. Performance is assessed on syntax correctness, ensuring grammar and executability, and content correctness, verifying accurate state representation in generated PDDL problems. The proposed approach demonstrates promising results across diverse task complexities, suggesting its potential for broader applications in AI planning. We will discuss a potential use case in robot-assisted teaching of students with Autism Spectrum Disorder.

The Center introduced three of the robots – two named Milo and another named Jemi – to its curriculum in January for students ages three to 21, Boerio said. While all teachers at the Center are trained in the new curriculum, the Center is primarily using School Psychology Program graduate assistants with the delivery. Both the facilitator and the student utilize an iPad, and one of the robots presents lessons through brief explanations, modeling and general facilitation.

This paper introduces the project Social Assistive Robotics for Autistic Children aimed at using robotic therapy for autism. The goal of the project is testing autistic children's interactions with the social robot NAO. In particular the robot will support the operators (psychologists, educators, speech therapists etc.) in their work. The innovative aspect of the project is that the children robot interaction will consider the children's emotions and specific features and the robot will adapt its behavior accordingly.

Sammy was diagnosed with Autism at a young age. I first met him through his Microsoft internship application and followed him on his path to becoming a full-time employee. Sammy is not his real name, but his experience is very real. Like many people with a disability, he has felt excluded from society at times, first at school when he wanted to help fellow students with autism, but the administration discouraged him from doing so because of his disability. As he grew up, he worried these imposed limitations would carry over into employment, so he learned to mask his disability to avoid the stigma he might encounter in job interviews.

Machines now perform all sorts of tasks: They clean big stores, patrol borders, and help autistic children. But will they make life better for humans?

Machines now perform all sorts of tasks: They clean big stores, patrol borders, and help autistic children. But will they make life better for humans?

Researchers have developed a new personalized learning robot for autistic children that uses machine learning to adapt its lessons to each kid's changing needs. The University of Southern California team put a "socially assistive robot" called Kiwi in the homes of 17 autistic children and set the two-foot-tall, green-feathered robot to give each child personalized classes. Over the course of a month, the children played space-themed math games on a tablet device while Kiwi provided feedback and instruction, such as congratulating them on a correct answer or giving tips after a wrong one. As the lessons progressed, algorithms adjusted Kiwi's feedback and the difficulty of the games to the child's individual needs. By the end of the month, all of the children had improved their math skills, while 92% had also improved their social skills.

MIT scientists have developed a new type of "deep learning" network that can aid robots gauge the quality of their interactions with children having autism spectrum conditions by using data unique to each child. Autism spectrum disorder is a condition related to brain development that impacts how a person perceives and socializes with others, causing problems in social interaction and communication. The term "spectrum" in autism spectrum disorder refers to the wide range of symptoms and severity. Armed with personalised "deep learning", the child-friendly robot NAO can smoothly estimate the engagement and interest of each autistic child, using data unique to that particular individual, based on a study performed on 35 autistic children. The new development can make their lives easier.

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. KASPAR (Kinesics and Synchronization in Personal Assistant Robotics) is a robot originally conceived as part of a research project begun in the late 1990s by artificial intelligence researcher Kerstin Dautenhahn and her collaborators at the University of Reading in England. Initially, the objective was to develop "robotic therapy games" to facilitate communication with autistic children and to help them interact with others. In 2005, now at the University of Hertfordshire, the KASPAR Project was formally launched with the aim of developing a "social" robot having two missions: first, and mainly, to be a "social mediator" responsible for facilitating communication between autistic children and the people with whom they are in daily contact--other children (autistic or not), therapists, teachers, and parents--and also to serve as a therapeutic and learning tool designed to stimulate social development in these children. The objective was to teach young people with autism a variety of skills that most of us master, more or less fully, without any need of special education: understanding others' emotions and reacting appropriately, expressing our own feelings, playing in a group while letting everyone take turns, and imitating and cooperating with others.