[Paper] A Spiking Neural Architecture for Coordinating Arm and Locomotor Control

Source: arXiv - 2606.11034v1

Overview

Spiking Neural Networks (SNNs) coupled with neuromorphic hardware offer energy-efficient solutions for humanoid robot control. However, existing SNN-based motor control systems address bipedal locomotion and arm control in isolation, leaving integrated control of both unaddressed. We present a spiking architecture that coordinates force-based arm control and bipedal locomotion in a simulated humanoid, using the Neural Engineering Framework (NEF) and Semantic Pointer Architecture (SPA). High-level action selection between locomotor and arm control is mediated by a biologically grounded spiking basal ganglia model. We validate the system through co-simulation of Nengo, for the neural control, and Isaac Sim, demonstrating successful target reaching, continuous digit drawing, path-following locomotion, and finally, switching between walking and arm control via basal ganglia disinhibition. To our knowledge, this is the first integrated spiking controller to combine bipedal locomotion and arm control on a full-scale humanoid platform. The full spike-based implementation enables future deployment on low-power neuromorphic hardware.

Key Contributions

This paper presents research in the following areas:

• cs.RO
• cs.NE

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.RO.

Authors

• Lea Steffen
• Kathryn Simone
• Graeme Damberger
• Travis DeWolf
• Hudson Ly
• Chris Eliasmith

Paper Information

• arXiv ID: 2606.11034v1
• Categories: cs.RO, cs.NE
• Published: June 9, 2026
• PDF: Download PDF