Embodied Neuromorphic Control Applied on a 7-DOF Robotic Manipulator

2025-04-17

AI Summary

To address the limitations of low accuracy, manual parameter tuning, and high computational overhead in inverse dynamics control of 7-DOF robotic arms, this paper proposes the first brain-inspired spiking neural network (SNN) control framework tailored for high-degree-of-freedom systems. The method integrates spatiotemporal modeling of continuous motion with dynamics-driven learning and is hardware-aware for neuromorphic platforms, enabling end-to-end joint torque prediction and real-time closed-loop control. Departing from prior works constrained to low-DOF setups and lacking quantitative validation, our approach is the first to demonstrate full closed-loop task execution on a physical 7-DOF robotic arm. Experimental results show over 60% reduction in torque prediction error, significantly improved target position tracking accuracy, and reduced power consumption and inference latency—advancing brain-inspired control toward practical embodied intelligence applications.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The development of artificial intelligence towards real-time interaction with the environment is a key aspect of embodied intelligence and robotics. Inverse dynamics is a fundamental robotics problem, which maps from joint space to torque space of robotic systems. Traditional methods for solving it rely on direct physical modeling of robots which is difficult or even impossible due to nonlinearity and external disturbance. Recently, data-based model-learning algorithms are adopted to address this issue. However, they often require manual parameter tuning and high computational costs. Neuromorphic computing is inherently suitable to process spatiotemporal features in robot motion control at extremely low costs. However, current research is still in its infancy: existing works control only low-degree-of-freedom systems and lack performance quantification and comparison. In this paper, we propose a neuromorphic control framework to control 7 degree-of-freedom robotic manipulators. We use Spiking Neural Network to leverage the spatiotemporal continuity of the motion data to improve control accuracy, and eliminate manual parameters tuning. We validated the algorithm on two robotic platforms, which reduces torque prediction error by at least 60% and performs a target position tracking task successfully. This work advances embodied neuromorphic control by one step forward from proof of concept to applications in complex real-world tasks.

Problem

• Solving inverse dynamics in robotics using neuromorphic computing
• Reducing computational costs and manual tuning in robot control
• Improving accuracy in high-degree-of-freedom robotic manipulators

Innovation

• Neuromorphic control for 7-DOF robotic manipulators
• Spiking Neural Network improves motion accuracy
• Eliminates manual tuning, reduces torque error