Teleology-Driven Affective Computing: A Causal Framework for Sustained Well-Being

Affective computing has made significant strides in emotion recognition and generation, yet current approaches mainly focus on short-term pattern recognition and lack a comprehensive framework to guide affective agents toward long-term human well-being. To address this, we propose a teleology-driven affective computing framework that unifies major emotion theories (basic emotion, appraisal, and constructivist approaches) under the premise that affect is an adaptive, goal-directed process that facilitates survival and development. Our framework emphasizes aligning agent responses with both personal/individual and group/collective well-being over extended timescales. We advocate for creating a "dataverse" of personal affective events, capturing the interplay between beliefs, goals, actions, and outcomes through real-world experience sampling and immersive virtual reality. By leveraging causal modeling, this "dataverse" enables AI systems to infer individuals' unique affective concerns and provide tailored interventions for sustained well-being. Additionally, we introduce a meta-reinforcement learning paradigm to train agents in simulated environments, allowing them to adapt to evolving affective concerns and balance hierarchical goals - from immediate emotional needs to long-term self-actualization. This framework shifts the focus from statistical correlations to causal reasoning, enhancing agents' ability to predict and respond proactively to emotional challenges, and offers a foundation for developing personalized, ethically aligned affective systems that promote meaningful human-AI interactions and societal well-being.

Robot Body Schema Learning from Full-body Extero/Proprioception Sensors

For a robot, its body structure is an a-prior knowledge when it is designed. However, when such information is not available, can a robot recognize it by itself? In this paper, we aim to grant a robot such ability to learn its body structure from exteroception and proprioception data collected from on-body sensors. By a novel machine learning method, the robot can learn a binary Heterogeneous Dependency Matrix from its sensor readings. We showed such matrix is equivalent to a Heterogeneous out-tree structure which can uniquely represent the robot body topology. We explored the properties of such matrix and the out-tree, and proposed a remedy to fix them when they are contaminated by partial observability or data noise. We ran our algorithm on 6 different robots with different body structures in simulation and 1 real robot. Our algorithm correctly recognized their body structures with only on-body sensor readings but no topology prior knowledge.