Robust Scene and Object Generalization of Neural Policies Trained in Synthetic Environments
Achieving generalization for autonomous robotic systems operating in real-world environments remains a significant challenge. Training robots solely in simulations can be limiting due to the "sim-to-real gap"– discrepancies between simulated and real-world conditions. We present two novel...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Published: |
Massachusetts Institute of Technology
2024
|
| Online Access: | https://hdl.handle.net/1721.1/156571 |