Efficient Learning Control via Structural Policy Priors, Latent World Models and Hierarchical Abstraction
Learning control will enable the deployment of autonomous robots in unstructured real-world settings. Solving the associated complex decision processes under real-time constraints will require intuition, guiding current actions by prior experience to anticipate long-horizon environment interactions...
Main Author: | Seyde, Tim N. |
---|---|
Other Authors: | Rus, Daniela |
Format: | Thesis |
Published: |
Massachusetts Institute of Technology
2024
|
Online Access: | https://hdl.handle.net/1721.1/156611 |
Similar Items
-
Latent hierarchical structural learning for object detection
by: Zhu, Long, et al.
Published: (2012) -
Learning to Plan via Deep Optimistic Value Exploration
by: Seyde, Tim, et al.
Published: (2020) -
Inductive Biases in Learning Hierarchical Abstractions for Bipedal Locomotion
by: Ravichandar, Sanjna
Published: (2024) -
Modeling latent information in voting data with Dirichlet process priors
by: Traunmüller, R, et al.
Published: (2014) -
Formal reasoning ability, prior knowledge and abstract achievement in the learning of Meiosis and genetics
by: Zaman, Sara
Published: (2008)