Language Models in Robotics

Chain of Hindsight

• Chain of Hindsight (CoH) is used to improve the outputs of language models.
• It presents a sequence of past outputs with feedback.
• To avoid overfitting, CoH uses a regularization term and masks past tokens.

PaLM-E

• PaLM-E is a multimodal language model that controls real robots.
• It can perform long-horizon tasks such as mobile manipulation in a kitchen.
• It exhibits one-shot and zero-shot generalization with a tabletop manipulation robot.

Inner Monologue

• Inner Monologue leverages a collection of perception models and pretrained language-conditioned robot skills to enable grounded closed-loop feedback for robot planning with large language models.
• It uses different types of textual feedback, such as success detection, passive scene description, and active scene description.

Language Models as Zero-Shot Planners

• Large language models (LLMs) can be used to extract actionable knowledge for embodied agents.
• LLMs decompose high-level tasks into sensible mid-level action plans.
• They translate each action plan step into an admissible action via another pre-trained masked LLM.

LLM Agents

• LLM Agents iterate and work towards a goal by feeding the results of their actions back into the prompt.

ReAct

• ReAct is a language model framework that synergizes reasoning and acting.
• It allows LLMs to decide what to do and feed the results back into the prompt.

Tools & LLMs

• Humans use tools to enhance scalability, consistency, interpretability, and productivity.
• LLMs also have similar limitations and can benefit from tool use.
• LLMs used with tools can also improve scalability, consistency, interpretability, and productivity.

Code as Policies

• Code as Policies uses language models to create programs for embodied control.
• It allows LLMs to ground language into executable actions in environments like databases, web applications, and robotic physical worlds.

TALM & Toolformer

• TALM (Tool Augmented Language Models) trains LLMs to use tools.
• Toolformer is a method where LLMs teach themselves to use tools.

API-Bank

• API-Bank is a benchmark for evaluating tool-augmented LLMs.
• It contains 53 common API tools, a complete workflow, and annotated dialogues involving API calls.
• Evaluation indices in API-Bank include accuracy, Rouge scores, and number of turns.

GPT4Tools

• GPT4Tools is a framework for teaching large language models to use tools via self-instruction.
• It measures the success rate of thought, action, arguments, and overall execution of action chains.

Binding Language Models in Symbolic Language

• It focuses on binding language models in symbolic languages to improve reasoning and problem-solving capabilities.

LATM

• LATM (Large Language Models as Tool Makers) allows LLMs to create new tools.

Reflexion

• Reflexion gives agents dynamic memory and self-reflection capabilities to improve reasoning skills.
• It uses a standard RL setup with task-specific action space augmented with language to enable complex reasoning steps.
• Agents compute a heuristic and may decide to reset the environment based on self-reflection results.
• It provides two-shot examples (failed trajectory, ideal reflection) to LLMs for learning self-reflection.
• Reflections are added to the agent's working memory for context in their subsequent queries to the LLM.

Explore the innovative applications of language models in robotics, focusing on advanced concepts such as Chain of Hindsight, PaLM-E, Inner Monologue, and zero-shot planning. This quiz delves into the mechanisms and benefits of integrating language models with robotic systems for enhanced task execution and feedback. Test your knowledge on these cutting-edge technologies.