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Incremental learning control of the DLR-HIT-Hand II during interaction tasks
Alessio Alessi1, Loredana Zollo, Luca Lonini
1Laboratory of Biomedical Robotics and Biomicrosystems, Università Campus Bio-Medico, via Alvaro del Portillo 21, 00128 Roma, Italy.
Summary
This study presents a bio-inspired robotic hand control system that learns from human interaction. It uses Locally Weighted Projection Regression (LWPR) networks for efficient, incremental learning, improving robotic hand dexterity.
Area of Science:
- Robotics
- Neuroscience
- Machine Learning
Background:
- Robotic hands require sophisticated control systems for complex tasks.
- Human motor control offers insights into efficient learning mechanisms.
- Internal models are crucial for predicting and adapting to environmental interactions.
Purpose of the Study:
- To introduce a bio-inspired control architecture for robotic hands.
- To enable the robotic hand to develop and update an internal representation of its interaction with the environment.
- To validate the control architecture's effectiveness in dynamic conditions.
Main Methods:
- Utilizing learning inverse internal models, inspired by human motor control.
- Employing Locally Weighted Projection Regression (LWPR) networks for efficient, incremental online learning.
- Validating the architecture on a simulated DLR-HIT-Hand II finger with viscous force fields.
Main Results:
- The control architecture successfully developed an internal representation of the hand-environment interaction.
- LWPR networks facilitated efficient learning and adaptation to external forces.
- The system demonstrated robust performance in simulated closing movements under perturbing force fields.
Conclusions:
- The bio-inspired control architecture effectively mimics human learning mechanisms for robotic hands.
- LWPR networks provide an efficient and adaptable learning paradigm for robotics.
- This approach enhances robotic hand control in dynamic and interactive environments.
