arXiv subjects

Michael J. Bennington

Publications and source records attributed to Michael J. Bennington.

3 recordsLinked to original sources

arXiv2026

NeuroAI and Beyond: Bridging Between Advances in Neuroscience and ArtificialIntelligence

Neuroscience and Artificial Intelligence (AI) have made impressive progress in recent years but remain only loosely interconnected. Based on a workshop convened by the National Science Foundation in August 2025, we identify three fundamental capability gaps in current AI: the inability to interact with the physical world, inadequate learning that produces brittle systems, and unsustainable energy and data inefficiency. We describe the neuroscience principles that address each: co-design of body and controller, prediction through interaction, multi-scale learning with neuromodulatory control, hierarchical distributed architectures, and sparse event-driven computation. We present a research roadmap organized around these principles at near, mid, and long-term horizons. We argue that realizing this program requires a new generation of researchers trained across the boundary between neuroscience and engineering, and describe the institutional conditions: interdisciplinary training, hardware access, community standards, and ethics, needed to support them. We conclude that NeuroAI, neuroscience-informed artificial intelligence, has the potential to overcome limitations of current AI while deepening our understanding of biological neural computation.

q-bio.NC↗

arXiv2025

PB&J: Peanut Butter and Joints for Damped Articulation

Many bioinspired robots mimic the rigid articulated joint structure of the human hand for grasping tasks, but experience high-frequency mechanical perturbations that can destabilize the system and negatively affect precision without a high-frequency controller. Despite having bandwidth-limited controllers that experience time delays between sensing and actuation, biological systems can respond successfully to and mitigate these high-frequency perturbations. Human joints include damping and stiffness that many rigid articulated bioinspired hand robots lack. To enable researchers to explore the effects of joint viscoelasticity in joint control, we developed a human-hand-inspired grasping robot with viscoelastic structures that utilizes accessible and bioderived materials to reduce the economic and environmental impact of prototyping novel robotic systems. We demonstrate that an elastic element at the finger joints is necessary to achieve concurrent flexion, which enables secure grasping of spherical objects. To significantly damp the manufactured finger joints, we modeled, manufactured, and characterized rotary dampers using peanut butter as an organic analog joint working fluid. Finally, we demonstrated that a real-time position-based controller could be used to successfully catch a lightweight falling ball. We developed this open-source, low-cost grasping platform that abstracts the morphological and mechanical properties of the human hand to enable researchers to explore questions about biomechanics in roboto that would otherwise be difficult to test in simulation or modeling.

cs.RO↗

arXiv2023

Design and Characterization of Viscoelastic McKibben Actuators with Tunable Force-Velocity Curves

The McKibben pneumatic artificial muscle is a commonly studied soft robotic actuator, and its quasistatic force-length properties have been well characterized and modeled. However, its damping and force-velocity properties are less well studied. Understanding these properties will allow for more robust dynamic modeling of soft robotic systems. The force-velocity response of these actuators is of particular interest because these actuators are often used as hardware models of skeletal muscles for bioinspired robots, and this force-velocity relationship is fundamental to muscle physiology. In this work, we investigated the force-velocity response of McKibben actuators and the ability to tune this response through the use of viscoelastic polymer sheaths. These viscoelastic McKibben actuators (VMAs) were characterized using iso-velocity experiments inspired by skeletal muscle physiology tests. A simplified 1D model of the actuators was developed to connect the shape of the force-velocity curve to the material parameters of the actuator and sheaths. Using these viscoelastic materials, we were able to modulate the shape and magnitude of the actuators' force-velocity curves, and using the developed model, these changes were connected back to the material properties of the sheaths.

cs.RO↗