Human 0, MLLM 1: Unlocking New Layers of Automation in Language-Conditioned Robotics with Multimodal LLMs
Bibliographic record
Abstract
Language-conditioned robotics has seen tremendous growth in frameworks that aim to improve the success rates of robots acting upon the environment according to free-form language instructions. However, most existing frameworks leverage a human in the loop to assist with critical functions. Humans are mainly involved in ensuring that a human-requested task is feasible, resetting the robot when it diverges from achieving the requested goal, and deciding if it has completed the task. As human involvement limits the scalability of language-conditioned robotics, we propose automating these human functions through Multimodal Large Language Models in the Loop (MLLM-IL). We conduct experiments leveraging multimodal large language models, specifically OpenAI's GPT-4, and Google Gemini, to evaluate their potential in automating crucial functions. The introduced new layers of automation include analyzing task feasibility, assessing task progress, and detecting task success. We investigate how different factors, including the choice of LLM, image resolution of the input images, and the structure of the prompt, affect the performance of the LLMs in achieving the target functions. Results show significant zero-shot success with feasibility analysis accuracies exceeding 90%. Our work demonstrates the immense potential of utilizing MLLM-IL to complement existing frameworks in language-conditioned robotics, opening the space for a wealth of new applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".