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Time to Play: Simulating Early-Life Animal Dynamics Enhances Robotics Locomotion Discovery

Published: September 15, 2025 | arXiv ID: 2509.11755v1

By: Paul Templier , Hannah Janmohamed , David Labonte and more

Potential Business Impact:

Robots learn to move better by growing stronger.

Business Areas:
Robotics Hardware, Science and Engineering, Software

Developmental changes in body morphology profoundly shape locomotion in animals, yet artificial agents and robots are typically trained under static physical parameters. Inspired by ontogenetic scaling of muscle power in biology, we propose Scaling Mechanical Output over Lifetime (SMOL), a novel curriculum that dynamically modulates robot actuator strength to mimic natural variations in power-to-weight ratio during growth and ageing. Integrating SMOL into the MAP-Elites quality-diversity framework, we vary the torque in standard robotics tasks to mimic the evolution of strength in animals as they grow up and as their body changes. Through comprehensive empirical evaluation, we show that the SMOL schedule consistently elevates both performance and diversity of locomotion behaviours across varied control scenarios, by allowing agents to leverage advantageous physics early on to discover skills that act as stepping stones when they reach their final standard body properties. Based on studies of the total power output in humans, we also implement the SMOL-Human schedule that models isometric body variations due to non-linear changes like puberty, and study its impact on robotics locomotion.

Country of Origin
🇬🇧 United Kingdom

Page Count
10 pages

Category
Computer Science:
Neural and Evolutionary Computing