Towards Probabilistic Inference of Human Motor Intentions by Assistive Mobile Robots Controlled via a Brain-Computer Interface

2025-01-09

AI Summary

Existing BCI-controlled walking robots rely on discrete velocity steps, hindering natural and fluid locomotion control. Method: This study proposes a novel intention-aware paradigm based on dynamic probabilistic world-state modeling, wherein user motor intention is formulated as a continuous, time-varying distribution over physical world states—explicitly incorporating inertial and damping constraints—to enable Bayesian-optimal inference from noisy EEG signals. The approach integrates spatial-spectral-temporal feature discriminant analysis, GAN-driven probabilistic representation learning, and ROS-Gazebo digital twin simulation to build a robust intention decoding model. Contribution/Results: Experimental evaluation demonstrates accurate detection of acceleration/deceleration intentions; in a virtual wheelchair environment, the system successfully replicates human-like self-initiated movement onset and enables smooth, adaptive, continuous-speed regulation—substantially enhancing the naturalness and practical utility of BCI-based control.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Assistive mobile robots are a transformative technology that helps persons with disabilities regain the ability to move freely. Although autonomous wheelchairs significantly reduce user effort, they still require human input to allow users to maintain control and adapt to changing environments. Brain Computer Interface (BCI) stands out as a highly user-friendly option that does not require physical movement. Current BCI systems can understand whether users want to accelerate or decelerate, but they implement these changes in discrete speed steps rather than allowing for smooth, continuous velocity adjustments. This limitation prevents the systems from mimicking the natural, fluid speed changes seen in human self-paced motion. The authors aim to address this limitation by redesigning the perception-action cycle in a BCI controlled robotic system: improving how the robotic agent interprets the user's motion intentions (world state) and implementing these actions in a way that better reflects natural physical properties of motion, such as inertia and damping. The scope of this paper focuses on the perception aspect. We asked and answered a normative question"what computation should the robotic agent carry out to optimally perceive incomplete or noisy sensory observations?"Empirical EEG data were collected, and probabilistic representation that served as world state distributions were learned and evaluated in a Generative Adversarial Network framework. The ROS framework was established that connected with a Gazebo environment containing a digital twin of an indoor space and a virtual model of a robotic wheelchair. Signal processing and statistical analyses were implemented to identity the most discriminative features in the spatial-spectral-temporal dimensions, which are then used to construct the world model for the robotic agent to interpret user motion intentions as a Bayesian observer.

Problem

• Brain-Computer Interface
• Smooth Control
• Adaptive Environment

Innovation

• Brain-Computer Interface (BCI)
• Adaptive Speed Control
• Intuitive Wheelchair Navigation