RMTL decomposes long-horizon Fetch manipulation into three micro-tasks with per-stage VLM rewards, a reverse curriculum, and a learned hierarchical manager, yielding faster learning than single-prompt VLM rewards.
arXiv preprint arXiv:2310.07899 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
The paper defines Agent AI as interactive multimodal systems that perceive grounded data and generate embodied actions, arguing this approach can mitigate hallucinations in foundation models.
citing papers explorer
-
RMTL: Reinforced Micro-task Learning for Long-Horizon Manipulation with VLM Rewards
RMTL decomposes long-horizon Fetch manipulation into three micro-tasks with per-stage VLM rewards, a reverse curriculum, and a learned hierarchical manager, yielding faster learning than single-prompt VLM rewards.
-
Agent AI: Surveying the Horizons of Multimodal Interaction
The paper defines Agent AI as interactive multimodal systems that perceive grounded data and generate embodied actions, arguing this approach can mitigate hallucinations in foundation models.