Search Results for author:
Found 5 papers, 1 papers with code
Human-Timescale Adaptation in an Open-Ended Task Space
Foundation models have shown impressive adaptation and scalability in supervised and self-supervised learning problems, but so far these successes have not fully translated to reinforcement learning (RL).
Policy Manifold Search: Exploring the Manifold Hypothesis for Diversity-based Neuroevolution
This paper proposes a novel method for diversity-based policy search via Neuroevolution, that leverages learned representations of the policy network parameters, by performing policy search in this learned representation space.
Policy Manifold Search for Improving Diversity-based Neuroevolution
Our approach iteratively collects policies according to the QD framework, in order to (i) build a collection of diverse policies, (ii) use it to learn a latent representation of the policy parameters, (iii) perform policy search in the learned latent space.
Sim-to-Real Learning for Casualty Detection from Ground Projected Point Cloud Data
This network is trained to be able to detect a casualty using a point-cloud data input.
Active learning via informed search in movement parameter space for efficient robot task learning and transfer
We propose a novel active learning framework, consisting of decoupled task model and exploration components, which does not require an objective function.
