Large-scale evolutionary simulations by DERL yield insights into how the interaction between learning, evolution, and environmental complexity can lead to morphological intelligence.
The Object-centric perception, prediction, and planning (OP3) framework demonstrates strong generalization to novel configurations in block stacking tasks by symmetrically processing entity representations extracted from raw visual observations.
The Control What You Can (CWYC) method learns to control components of the environment to achieve multi-step goals by combining task planning with surprise and learning progress based intrinsic motivation.
Proposes multitask model to jointly learn semantic goal navigation and embodied question answering.
Applies curriculum learning to a RL context to achieve policies.