We present COmpetitive Mechanisms for Efficient Transfer (COMET), a modular world model which leverages reusable, independent mechanisms across different environments. COMET is trained on multiple environments with varying dynamics via a two-step process: competition and composition. This enables the model to recognise and learn transferable mechanisms. Specifically, in the competition phase, COMET is trained with a winner-takes-all gradient allocation, encouraging the emergence of independent mechanisms. These are then re-used in the composition phase, where COMET learns to re-compose learnt mechanisms in ways that capture the dynamics of intervened environments. In so doing, COMET explicitly reuses prior knowledge, enabling efficient and interpretable adaptation. We evaluate COMET on environments with image-based observations. In contrast to competitive baselines, we demonstrate that COMET captures recognisable mechanisms without supervision. Moreover, we show that COMET is able to adapt to new environments with varying numbers of objects with improved sample efficiency compared to more conventional finetuning approaches.

This work deals with designing simple behaviors to allow quadruped robots to play soccer. The robots are fully autonomous; they cannot exchange messages between each other. They are equipped with a charge-coupled-device camera that allows them to detect objects in the scene. In addition to vision problems such as changing lighting conditions and color confusion, legged robots must cope with "bouncing images" because of successive legs hitting the ground. When defining task-driven strategies, the designer has to take into account the influences of the locomotion and vision systems on the behavior.

This work deals with designing simple behaviors to allow quadruped robots to play soccer. The robots are fully autonomous; they cannot exchange messages between each other. They are equipped with a charge-coupled-device camera that allows them to detect objects in the scene. In addition to vision problems such as changing lighting conditions and color confusion, legged robots must cope with "bouncing images" because of successive legs hitting the ground. When defining task-driven strategies, the designer has to take into account the influences of the locomotion and vision systems on the behavior. Locomotion and vision skills should be made as reliable as possible. Because it is not always possible to simulate the problems encountered in real situations, the behavior strategy should anticipate them. In this article, we describe all the behaviors used to play soccer games on a soccer field surrounded with landmarks. Experiments were carried out at the 1999 RoboCup in Stockholm using the Sony quadruped robots (Fujita 2000).