Walking and falling: Using robot simulations to model the role of errors in infant walking
Abstract
What is the optimal penalty for errors in infant skill learning? Behavioral analyses indicate that errors are frequent but trivial as infants acquire foundational skills. In learning to walk, for example, falling is commonplace but appears to incur only a negligible penalty. Behavioral data, however, cannot reveal whether a low penalty for falling is beneficial for learning to walk. Here, we used a simulated bipedal robot as an embodied model to test the optimal penalty for errors in learning to walk. We trained the robot to walk using 12,500 independent simulations on walking paths produced by infants during free play and systematically varied the penalty for falling—a level of precision, control, and magnitude impossible with real infants. When trained with lower penalties for falling, the robot learned to walk farther and better on familiar, trained paths and better generalized its learning to novel, untrained paths. Indeed, zero penalty for errors led to the best performance for both learning and generalization. Moreover, the beneficial effects of a low penalty were stronger for generalization than for learning. Robot simulations corroborate prior behavioral data and suggest that a low penalty for errors helps infants learn foundational skills (e.g., walking, talking, and social interactions) that require immense flexibility, creativity, and adaptability.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 26, 2023
- Source ID
- 10.1111/desc.13449
Entities
People
- Danyang Han
- Justine E. Hoch
- Karen Adolph
- Ori Ossmy
- Patrick Macalpine
- Peter Stone
Organizations
- Birkbeck, University of London
- Defense Advanced Research Projects Agency
- Eunice Kennedy Shriver National Institute of Child Health and Human Development
- New York University
- Sony Group
- University of Texas at Austin
- Vanderbilt University