robots

Do robots need creativity in problem solving? Physical AI is a type of AI that enables robots to perceive and interact with a physical world. Early physical AI models follow the sense-plan-act framework where robots make decisions dominantly based on sensed information. While sensing is crucial for understanding the current physical environments, this paradigm of physical AI is fundamentally limited to support general tasks where humans solve problems creatively based on not only what they can observe now but also various predictions of the unseen spatiotemporal and social contexts, that is, seeing around corners. In this talk, I will illustrate why robots without creativity can fail to fulfill even simple goals and how we develop creative physical AI. 

If equipped with creative physical AI, can such robots promote human creativity as in creating arts? Generative AI has brought us numerous types of convenience in the digital art world. To create artifacts in the real world, creative physical AI is needed, for instance, to preserve traditional craftsmanship such as wood carving or claymation, which faces declining participation due to its labor-intensive nature. More broadly, our innovations in creative physical AI aim to encourage people to participate in more creative activities such as educational and therapeutic art sessions. I would like to invite the audience to think about how we can use technologies to promote human creativity for the next generation.

Institutions

• AI for Good

Despite significant advances in AI, robots still struggle with tasks involving physical interaction. Robots can easily beat humans at board games such as Chess or Go but struggle to skillfully move the game pieces by themselves (the part of the task that humans subconsciously succeed in). Learning manipulation skills is both hard and fascinating because the movements and behaviors to acquire are tightly connected to our physical world and to embodied forms of intelligence.

I will present an overview of representations and learning approaches to help robots acquire manipulation skills by imitation and self-refinement. I will present the advantages of targeting a frugal learning approach, where the term “frugality” has two goals: 1) learning manipulation skills from only few demonstrations or exploration trials; and 2) learning only the components of the skill that really need to be learned.

Toward this goal, I will emphasize the roles of geometry, manifolds, implicit shape representations and distance fields as inductive biases to facilitate human-guided manipulation skill acquisition. I will also show how ergodic control can provide a mathematical framework to generate exploration and coverage movement behaviors, which can be exploited by robots as a way to cope with uncertainty in sensing, proprioception and motor control.

Institutions

• AI for Good