Monthly Archives: May 2024

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A survey on neurosymbolic RL and planning

K. Acharya, W. Raza, C. Dourado, A. Velasquez and H. H. Song, Neurosymbolic Reinforcement Learning and Planning: A Survey, IEEE Transactions on Artificial Intelligence, vol. 5, no. 5, pp. 1939-1953, May 2024 DOI: 10.1109/TAI.2023.3311428.

The area of neurosymbolic artificial intelligence (Neurosymbolic AI) is rapidly developing and has become a popular research topic, encompassing subfields, such as neurosymbolic deep learning and neurosymbolic reinforcement learning (Neurosymbolic RL). Compared with traditional learning methods, Neurosymbolic AI offers significant advantages by simplifying complexity and providing transparency and explainability. Reinforcement learning (RL), a long-standing artificial intelligence (AI) concept that mimics human behavior using rewards and punishment, is a fundamental component of Neurosymbolic RL, a recent integration of the two fields that has yielded promising results. The aim of this article is to contribute to the emerging field of Neurosymbolic RL by conducting a literature survey. Our evaluation focuses on the three components that constitute Neurosymbolic RL: neural, symbolic, and RL. We categorize works based on the role played by the neural and symbolic parts in RL, into three taxonomies: learning for reasoning, reasoning for learning, and learning–reasoning. These categories are further divided into subcategories based on their applications. Furthermore, we analyze the RL components of each research work, including the state space, action space, policy module, and RL algorithm. In addition, we identify research opportunities and challenges in various applications within this dynamic field.

Ultrasounds as an interface to read neuro-muscular signals

X. Yang, C. Castellini, D. Farina and H. Liu, Ultrasound as a Neurorobotic Interface: A Review, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 6, pp. 3534-3546, June 2024 DOI: 10.1109/TSMC.2024.3358960.

Neurorobotic devices, such as prostheses, exoskeletons, and muscle stimulators, can partly restore motor functions in individuals with disabilities, such as stroke, spinal cord injury (SCI), and amputations and musculoskeletal impairments. These devices require information transfer from and to the nervous system by neurorobotic interfaces. However, current interfacing systems have limitations of low-spatial and temporal resolution, and lack robustness, with sensitivity to, e.g., fatigue and sensor displacement. Muscle scanning and imaging by ultrasound technology has emerged as a neurorobotic interface alternative to more conventional electrophysiological recordings. While muscle ultrasound detects movement of muscle fibers, and therefore does not directly detect neural information, the muscle fibers are activated by neurons in the spinal cord and therefore their motions mirror the neural code sent from the spinal cord to muscles. In this view, muscle imaging by ultrasound provides information on the neural activation underlying movement intent and execution. Here, we critically review the literature on ultrasound applied as a neurorobotic interface, focusing on technological progresses and current achievements, machine learning algorithms, and applications in both upper- and lower- limb robotics. This critical review reveals that ultrasound in the human-machine interface field has evolved from bulky hardware to miniaturized systems, from multichannel imaging to sparse channel sensing, from simple muscle morphological analysis to input signal for musculoskeletal models and machine learning, from unimodal sensing to multimodal fusion, and from conventional statistical learning to deep learning. For future advances, we recommend exploring high-precision ultrasound imaging technology, improving the wearability and ergonomics of systems and transducers, and developing user-friendly real-time human-machine interaction models.

An alternative conceptual basis for curiosity / motivation

Francesco Poli, Jill X. O’Reilly, Rogier B. Mars, Sabine Hunnius, Curiosity and the dynamics of optimal exploration, Trends in Cognitive Sciences, Volume 28, Issue 5, 2024, DOI: 10.1016/j.tics.2024.02.001.

What drives our curiosity remains an elusive and hotly debated issue, with multiple hypotheses proposed but a cohesive account yet to be established. This review discusses traditional and emergent theories that frame curiosity as a desire to know and a drive to learn, respectively. We adopt a model-based approach that maps the temporal dynamics of various factors underlying curiosity-based exploration, such as uncertainty, information gain, and learning progress. In so doing, we identify the limitations of past theories and posit an integrated account that harnesses their strengths in describing curiosity as a tool for optimal environmental exploration. In our unified account, curiosity serves as a ‘common currency’ for exploration, which must be balanced with other drives such as safety and hunger to achieve efficient action.