1 citations · 1 across the 8 of their papers we have counts for
8 papers
In Two Minds about Lifelong Learning: Exploring Hemispheric Redundancy and Specialisation in Neural Models
Benjamin Smith, Levin Kuhlmann, Kaushik Roy +1
Persistent intelligent systems require the ability to learn continually, but current machine learning approaches face significant challenges in this area compared to biological lea…
The Epistemic Politics of AI Anthropomorphism
Donna M. Bye, Levin Kuhlmann
AI anthropomorphism is typically treated as a problem of user misperception requiring institutional correction. Users who engage in sustained or relational interaction with AI are…
Communication in modular robotic motor control: Bilateral controllers under realistic constraints
Jingwen Li, Levin Kuhlmann, Jason Friedman +1
Robotic motor control in musculoskeletal systems requires fast, accurate movement and robust postural stabilization under signal-dependent noise (where motor command variance scale…
Not All Timesteps Matter Equally: Selective Alignment Knowledge Distillation for Spiking Neural Networks
Kai Sun, Peibo Duan, Yongsheng Huang +4
Spiking neural networks (SNNs), which are brain-inspired and spike-driven, achieve high energy efficiency. However, a performance gap between SNNs and artificial neural networks (A…
ARROW: Augmented Replay for RObust World models
Abdulaziz Alyahya, Abdallah Al Siyabi, Markus R. Ernst +3
Continual reinforcement learning challenges agents to acquire new skills while retaining previously learned ones with the goal of improving performance in both past and future task…
Graceful task adaptation with a bi-hemispheric RL agent
Grant Nicholas, Levin Kuhlmann, Gideon Kowadlo
In humans, responsibility for performing a task gradually shifts from the right hemisphere to the left. The Novelty-Routine Hypothesis (NRH) states that the right and left hemisphe…
Augmenting Replay in World Models for Continual Reinforcement Learning
Luke Yang, Levin Kuhlmann, Gideon Kowadlo
Continual RL requires an agent to learn new tasks without forgetting previous ones, while improving on both past and future tasks. The most common approaches use model-free algorit…
Left/Right Brain, human motor control and the implications for robotics
Jarrad Rinaldo, Levin Kuhlmann, Jason Friedman +1
Neural Network movement controllers promise a variety of advantages over conventional control methods, however, they are not widely adopted due to their inability to produce reliab…