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Found 8 papers, 2 papers with code
Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning
This method uses a dynamics model, learned from interactions between the robot and the environment, to predict the robot's behaviour and improve sample efficiency.
Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning
We present a Quality-Diversity benchmark suite for Deep Neuroevolution in Reinforcement Learning domains for robot control.
Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity
Quality-Diversity (QD) algorithms have been successfully used to make robots adapt to damages in seconds by leveraging a diverse set of learned skills.
Hierarchical Quality-Diversity for Online Damage Recovery
These adaptation capabilities are directly linked to the behavioural diversity in the repertoire.
Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous Robots
When the robot successfully executes the task, we use the attainment regions to gain insights into the limits of the controller, and its robustness.
Counterfactual Explanation and Causal Inference in Service of Robustness in Robot Control
', motivated by applications in robot control.
Semi-supervised Learning From Demonstration Through Program Synthesis: An Inspection Robot Case Study
The system induces a controller program by learning from immersive demonstrations using sequential importance sampling.
Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop
Research on intrinsic motivations may profit from an additional way to implement intrinsically motivated agents that also share the biological plausibility of active inference.
