What do we learn about development from baby robots?

Understanding infant development is one of the greatest scientific challenges of contemporary science. A large source of difficulty comes from the fact that the development of skills in infants results from the interactions of multiple mechanisms at multiple spatio-temporal scales. The concepts of “innate” or “acquired” are not any more adequate tools for explanations, which call for a shift from reductionist to systemic accounts. To address this challenge, building and experimenting with robots modeling the growing infant brain and body is crucial. Systemic explanations of pattern formation in sensorimotor, cognitive and social development, viewed as a complex dynamical system, require the use of formal models based on mathematics, algorithms and robots. Formulating hypothesis about development using such models, and exploring them through experiments, allows us to consider in detail the interaction between many mechanisms and parameters. This complements traditional experimental methods in psychology and neuroscience where only a few variables can be studied at the same time. Furthermore, the use of robots is of particular importance. The laws of physics generate everywhere around us spontaneous patterns in the inorganic world. They also strongly impact the living, and in particular constrain and guide infant development through the properties of its (changing) body in interaction with the physical environment. Being able to consider the body as an experimental variable, something that can be systematically changed in order to study the impact on skill formation, has been a dream to many developmental scientists. This is today becoming possible with developmental robotics.

💡 Research Summary

The paper argues that understanding infant development requires moving beyond the outdated dichotomy of “innate” versus “acquired” and adopting a systemic, dynamical‑systems perspective. Infant skill acquisition—whether sensorimotor, cognitive, or social—emerges from the interaction of many mechanisms operating across multiple spatial and temporal scales. Because such interactions are inherently multivariate, traditional reductionist experiments in psychology and neuroscience, which can only manipulate a handful of variables at a time, are insufficient for capturing the full picture.

To address this limitation, the authors propose the use of developmental robotics: robots that model the growing infant brain and body. These robots are equipped with sensors and actuators that mimic infant capabilities, yet they allow researchers to systematically vary bodily parameters such as limb length, joint stiffness, muscle strength, and sensory channel properties. By treating the body itself as an experimental variable, researchers can directly test how physical constraints—generated by the laws of physics—shape the emergence of motor patterns, perceptual learning, and social interaction. For example, shortening a robot’s arm changes the dynamics of visual‑motor coordination, slowing the convergence of a learning algorithm; altering simulated gravity modifies balance‑control strategies. Such manipulations are impossible or ethically problematic in human infants, but robots provide a repeatable, controllable platform that yields statistically robust data.

The authors emphasize that robotic experiments are not meant to replace human studies but to complement them. Findings from robot models generate precise, mathematically formalized hypotheses (e.g., differential equations describing sensorimotor loops) that can be tested against infant data. Conversely, observations from infants can inform the design of more biologically realistic robot controllers, creating a virtuous feedback loop between artificial and biological systems. This bidirectional approach promises to transform developmental science from a largely descriptive field into one capable of quantitative prediction.

Beyond the body, the paper highlights the importance of the physical environment. Because the same physical laws that govern inanimate matter also constrain living bodies, changing environmental parameters (e.g., surface friction, object affordances) in a robotic setup provides insight into how infants adapt to and are guided by their surroundings. The authors suggest future work that integrates more sophisticated neural network models, multi‑robot social interactions, and mixed reality environments to simulate the richness of real‑world developmental contexts.

In summary, the paper makes a compelling case that developmental robotics offers a unique, systematic method for probing the complex, multiscale interactions that drive infant development. By treating the body and environment as manipulable variables and grounding hypotheses in formal mathematics and algorithms, researchers can explore “what‑if” scenarios that are otherwise inaccessible, thereby enriching our theoretical understanding and guiding empirical investigations in human development.