Neural Associative Skill Memories for safer robotics and modelling human sensorimotor repertoires (2505.09760v1)

Abstract: Modern robots face challenges shared by humans, where machines must learn multiple sensorimotor skills and express them adaptively. Equipping robots with a human-like memory of how it feels to do multiple stereotypical movements can make robots more aware of normal operational states and help develop self-preserving safer robots. Associative Skill Memories (ASMs) aim to address this by linking movement primitives to sensory feedback, but existing implementations rely on hard-coded libraries of individual skills. A key unresolved problem is how a single neural network can learn a repertoire of skills while enabling fault detection and context-aware execution. Here we introduce Neural Associative Skill Memories (ASMs), a framework that utilises self-supervised predictive coding for temporal prediction to unify skill learning and expression, using biologically plausible learning rules. Unlike traditional ASMs which require explicit skill selection, Neural ASMs implicitly recognize and express skills through contextual inference, enabling fault detection across learned behaviours without an explicit skill selection mechanism. Compared to recurrent neural networks trained via backpropagation through time, our model achieves comparable qualitative performance in skill memory expression while using local learning rules and predicts a biologically relevant speed-accuracy trade-off during skill memory expression. This work advances the field of neurorobotics by demonstrating how predictive coding principles can model adaptive robot control and human motor preparation. By unifying fault detection, reactive control, skill memorisation and expression into a single energy-based architecture, Neural ASMs contribute to safer robotics and provide a computational lens to study biological sensorimotor learning.

Neural Associative Skill Memories for Robotics

This paper introduces a novel framework called Neural Associative Skill Memories (ASMs) which aims to provide a more adaptive and safer approach to robotic sensorimotor control by modeling certain principles underpinning human sensorimotor memory. The work seeks to leverage predictive coding for developing robotic systems that can learn, express, and detect faults across a repertoire of sensorimotor skills without explicit skill selection—a significant departure from traditional ASM approaches which necessitate hard-coded libraries of individual movement primitives.

The authors utilize self-supervised predictive coding networks that rely on biologically plausible local learning rules to allow robots to implicitly recognize and express skills through contextual inference. This contrasts with traditional ASMs that demand explicit skill selection from a separate module tracking dynamic movement primitives (DMPs) alongside associated sensory event memory systems.

Main Contributions

1. Temporally Predictive Associative Skill Memory: The authors present a predictive coding network architecture that encodes sensorimotor sequences as associative memories. This enables robots to retrieve and express these memories efficiently, aiding in context-aware execution and fault detection.
2. Efficient Fault Detection: By employing an energy-based model, the neural ASMs can detect deviations from expected sensorimotor sequences, identifying possible faults during execution. This predictive model supports real-time adjustments using local proprioceptive prediction error minimization.
3. Implicit Skill Recognition: Unlike previous models, the proposed framework does not require pre-defined libraries but rather integrates skill recognition directly into the network's inference dynamics. This allows for more flexible and seamless adaptation to sensory cues.
4. Predictive Coding Incorporation: Inspired by neuroscience's predictive coding theory—which proposes that the brain continually predicts sensory inputs based on a generative model—the framework approximates such processes to yield accurate temporal predictions and support multiple skill learning.
5. Simulation-Based Verification: The authors verify the framework's capabilities using a robotics simulation setup, showcasing the advantages in terms of dynamic skill retrieval, contextually-inferred execution, fault detection, and correction.

Implications and Future Work

This work implies potential advancements in neurorobotics, promoting safer and more resilient robotic systems by integrating sensory predictions with motor control strategies in a unified, locally learned framework. The ability to implicitly recognize and react to sensorimotor skills through contextual inference lowers dependence on explicit memory selection mechanisms, enhancing the adaptability of robotic systems similar to biological entities.

Future work could explore extending the neural ASM system to real-world robotics scenarios and examining more complex sensorimotor tasks. Improvements may include dynamically adaptive precision for sensor filtering under noisy conditions, integration with more energy-efficient control paradigms, and exploration of multi-timescale learning approaches to increase sequence memory capacity. Additionally, comparative analyses with other biologically plausible models, like fast-weight RNNs, might yield further insights into optimizing temporal processes analogous to human sensorimotor preparation and execution.

In summary, Neural ASMs offer a promising computational model to paper and implement biological sensorimotor learning principles, contributing to the development of safer and functionally versatile robotics inspired by human cognitive and motor systems.