Abstract
We present a representation for describing transition models in complex uncertain domains using relational rules. For any action, a rule selects a set of relevant objects and computes a distribution over properties of just those objects in the resulting state given their properties in the previous state. An iterative greedy algorithm is used to construct a set of deictic references that determine which objects are relevant in any given state. Feed-forward neural networks are used to learn the transition distribution on the relevant objects' properties. This strategy is demonstrated to be both more versatile and more sample efficient than learning a monolithic transition model in a simulated domain in which a robot pushes stacks of objects on a cluttered table.
| Original language | English (US) |
|---|---|
| State | Published - 2019 |
| Externally published | Yes |
| Event | 7th International Conference on Learning Representations, ICLR 2019 - New Orleans, United States Duration: May 6 2019 → May 9 2019 |
Conference
| Conference | 7th International Conference on Learning Representations, ICLR 2019 |
|---|---|
| Country/Territory | United States |
| City | New Orleans |
| Period | 5/6/19 → 5/9/19 |
All Science Journal Classification (ASJC) codes
- Education
- Computer Science Applications
- Linguistics and Language
- Language and Linguistics