Bidirectional Tutoring for Developmental Motor Learning in Robots: Co-Developed Interaction Dynamics Support Stable Learning
arXiv:2606.19728v1 Announce Type: cross Abstract: Infants are well known to develop their motor skills through dense interaction with caregivers. Although such social interaction is crucial for human development, motor-skill learning in robots is often treated as a unidirectional process in which robots passively receive demonstrations from tutors. This overlooks a key property of social interaction: it is inherently bidirectional, with tutor and learner dynamically adapting to each other. In such interactions, the robot's past experiences may function as prior constraints that shape the dynam
This research builds on contemporary understanding of AI development and developmental psychology, applying bidirectional learning principles to robotics at a time when embodied AI is a significant research frontier.
A strategic reader should care because bidirectional tutoring could significantly accelerate motor skill acquisition and robustness in robots, impacting their general utility and integration into complex human environments.
This paper introduces a more effective paradigm for robot learning, moving beyond passive data ingestion to active, adaptive co-development, potentially leading to more versatile and adaptable robotic systems.
- · Robotics companies
- · AI research institutions
- · Automation sector
- · Developers reliant on traditional unidirectional robot training methods
- · Industries requiring highly specialized, non-adaptive robotic solutions
Robots will learn complex motor skills more rapidly and with greater stability.
This enhanced learning capability could accelerate the deployment of humanoid and general-purpose robots in various sectors, including manufacturing, logistics, and elder care.
The development of more socially interactive and adaptive robots might shift public perception and acceptance of robotics, fostering closer human-robot collaboration.
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Read at arXiv cs.AI