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Goal Representations for Instruction Following (GRIF), a novel semi-supervised approach for training robots, has been proposed by a group of scientists led by Vivek Myers, Andre He, and others. This approach aims to bridge the gap between the use of natural language task specifications in language-conditioned behavioral cloning (LCBC) and the performance advantages of goal-conditioned…

In the field of robot learning, a primary objective is to develop generalist agents that can execute tasks under human instructions. Although natural language is considered an effective interface for humans to delegate tasks, training robots to follow language instructions is enormously challenging. Existing methods, such as language-conditioned behavioral cloning (LCBC), necessitates a human to…

The authors propose a solution to the inherent vulnerability of deep learning classifiers to adversarial examples. These are minute, human-imperceptible image perturbations that trick machine learning models into misclassifying the modified input. This paper introduces the problem of asymmetric certified robustness, which requires certified robustness for only one class, reflecting real-world adversarial scenarios. The paper…

AI-generated language models such as ChatGPT have become so well-structured and complex that they threaten the veracity of text content in various circles, driving the need for precise detection tools. Unfortunately, existing tools for detecting AI-generated text have proven insufficient, sometimes inaccurately classifying human-written content as AI-generated, thus leading to a false question about the…

Large Language Models (LLMs), AI models capable of performing general tasks by prompting, have gained prominence in 2023. Even as these models continue to improve, there has been a significant shift towards compound AI systems, which integrate multiple components. This shift has led to notable AI results and made it possible to obtain better AI…