# GT-RIPL/Awesome-LLM-Robotics

A comprehensive list of papers using large language/multi-modal models for Robotics/RL, including papers, codes, and related websites

Repository: https://github.com/GT-RIPL/Awesome-LLM-Robotics
Canonical: https://ross.abutalabs.com/products/awesome-llm-robotics
License: BSD-3-Clause
License Family: permissive
Last push: 2026-07-17T20:28:18+00:00

## Health v2 (maintenance only)
Score: 74/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 93, release rhythm 35, longevity 100
- inputs: {"age_days": 1482, "days_push": 47, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4455, forks 340 (observed 2026-08-28T04:08:50.579393+00:00)

## What it is
A curated awesome-list of research papers, code, and websites applying large language and multi-modal models to robotics and reinforcement learning. It organizes resources by topic such as reasoning, planning, manipulation, navigation, simulation frameworks, and safety.

## Use cases
- find papers on LLMs for robotics
- research foundation models for robot manipulation
- survey LLM-based robot planning and reasoning
- find robotics simulation frameworks using multimodal models
- keep up with LLM robotics research
- find safety and red-teaming research for embodied AI

## When to choose
- you need a curated, categorized reading list of LLM+robotics research
- you are starting a literature review on language-conditioned robotics
- you want links to papers, code, and project websites in one place

## When to avoid
- you need runnable software rather than a paper list
- you need exhaustive coverage of all robotics research beyond LLM/multimodal methods

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation
- domain: robotics, artificial-intelligence, large-language-models, reinforcement-learning, tutorials
- platform: cross-platform
- tags: awesome-list, papers, robotics, llm, curated-list, research

## Member repositories
- GT-RIPL/Awesome-LLM-Robotics (main) score 74

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:50.579393+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:20:44.736464+00:00, confidence not recorded.
  - readme: https://github.com/GT-RIPL/Awesome-LLM-Robotics (fetched 2026-08-28T04:08:50.579393+00:00, sha 82395cfc7da1)
- Data as of 2026-08-30T08:39:29.467469+00:00.