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Found 4 papers, 1 papers with code
Video-Language Critic: Transferable Reward Functions for Language-Conditioned Robotics
Natural language is often the easiest and most convenient modality for humans to specify tasks for robots.
Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search
In this work we consider Code World Models, world models generated by a Large Language Model (LLM) in the form of Python code for model-based Reinforcement Learning (RL).
Learning Reward Functions for Robotic Manipulation by Observing Humans
Unlike prior work on leveraging human videos to teach robots, our method, Human Offline Learned Distances (HOLD) requires neither a priori data from the robot environment, nor a set of task-specific human demonstrations, nor a predefined notion of correspondence across morphologies, yet it is able to accelerate training of several manipulation tasks on a simulated robot arm compared to using only a sparse reward obtained from task completion.
Residual Reinforcement Learning from Demonstrations
Residual reinforcement learning (RL) has been proposed as a way to solve challenging robotic tasks by adapting control actions from a conventional feedback controller to maximize a reward signal.
