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High-probability grants
According to our matching algorithm, Kingson Man is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
2020 — 2022 |
Damasio, Antonio (co-PI) [⬀] Man, Kingson |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Nsf2026: Eager: Homeostasis and Soft Robotics For the Construction of Feeling Machines @ University of Southern California
With support from the Robust Intelligence program in the Division of Information and Intelligent Systems and the NSF 2026 Fund Program in the Office of Integrated Activities, investigators at University of Southern California are investigating a radical new approach to creating machines with the foundations for nature-inspired feeling as a primary basis for its action in the world. Recent theoretical work has raised the possibility that an analogue of feelings could be constructed in robots with bodies that are physically vulnerable to the environment. The possession of a body that can exist in better or worse condition, and the capacity to recognize and respond to such states are key elements in the generation of feeling in living creatures. This project will study a new class of machines organized according to the principles of life regulation, or homeostasis. The fundamental innovation of these machines is the introduction of risk-to-self. This project will explore how robots might respond to risk-to-self based on homeostasis rather than rely on the robustness provided primarily by e.g. increases in computation or protective encasement. The work aims to explore the nature-inspired question of the material basis for feelings and their potential value to an AI agent. By investigating machine-based homeostasis as the foundation of machine ?feeling? this project might lay the foundations for safer and perhaps even ?empathetic? robots in the spirit of the 2026 Idea Machine entry, ?Promoting Empathy-Based AI?.
This project will integrate recent breakthroughs from two disparate research fields: soft robotics and deep learning for multisensory integration. The field of soft robotics presents exciting new opportunities for machines to generate homeostatic data that are far richer than those achievable in rigid-bodied robots. The special properties of soft materials allow for greater flexibility, compliance, self-repair, and dense multimodal sensing. The tools of deep learning will be specifically applied to build multimodal representations that can bridge across internal and external sensory representations. The studies will be performed in computer simulations of voxel-based soft robots controlled by cross-modal neural networks. The behavioral advantages that accrue to soft robots possessing a homeostatic architecture will be quantified, both for robots in isolation and for societies of robots. This project's simulation work will lay the necessary preparations for future studies of machine feeling in physically realized robots.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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