8 citations · 14 across the 5 of their papers we have counts for
5 papers
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL
Yunseon Choi, Sangmin Bae, Seonghyun Ban +6
With the advent of foundation models, prompt tuning has positioned itself as an important technique for directing model behaviors and eliciting desired responses. Prompt tuning reg…
Empowering Large Language Models on Robotic Manipulation with Affordance Prompting
Guangran Cheng, Chuheng Zhang, Wenzhe Cai +3
While large language models (LLMs) are successful in completing various language processing tasks, they easily fail to interact with the physical world by generating control sequen…
ARO: Large Language Model Supervised Robotics Text2Skill Autonomous Learning
Yiwen Chen, Yuyao Ye, Ziyi Chen +2
Robotics learning highly relies on human expertise and efforts, such as demonstrations, design of reward functions in reinforcement learning, performance evaluation using human fee…
Pre-Trained Large Language Models for Industrial Control
Lei Song, Chuheng Zhang, Li Zhao +1
For industrial control, developing high-performance controllers with few samples and low technical debt is appealing. Foundation models, possessing rich prior knowledge obtained fr…
RePreM: Representation Pre-training with Masked Model for Reinforcement Learning
Yuanying Cai, Chuheng Zhang, Wei Shen +3
Inspired by the recent success of sequence modeling in RL and the use of masked language model for pre-training, we propose a masked model for pre-training in RL, RePreM (Represent…