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RoboGPT: an intelligent agent of making embodied long-term decisions for daily instruction tasks (2311.15649v3)

Published 27 Nov 2023 in cs.RO, cs.AI, and cs.LG

Abstract: Robotic agents must master common sense and long-term sequential decisions to solve daily tasks through natural language instruction. The developments in LLMs in natural language processing have inspired efforts to use LLMs in complex robot planning. Despite LLMs' great generalization and comprehension of instruction tasks, LLMs-generated task plans sometimes lack feasibility and correctness. To address the problem, we propose a RoboGPT agent\footnote{our code and dataset will be released soon} for making embodied long-term decisions for daily tasks, with two modules: 1) LLMs-based planning with re-plan to break the task into multiple sub-goals; 2) RoboSkill individually designed for sub-goals to learn better navigation and manipulation skills. The LLMs-based planning is enhanced with a new robotic dataset and re-plan, called RoboGPT. The new robotic dataset of 67k daily instruction tasks is gathered for fine-tuning the Llama model and obtaining RoboGPT. RoboGPT planner with strong generalization can plan hundreds of daily instruction tasks. Additionally, a low-computational Re-Plan module is designed to allow plans to flexibly adapt to the environment, thereby addressing the nomenclature diversity challenge. The proposed RoboGPT agent outperforms SOTA methods on the ALFRED daily tasks. Moreover, RoboGPT planner exceeds SOTA LLM-based planners like ChatGPT in task-planning rationality for hundreds of unseen daily tasks, and even other domain tasks, while keeping the large model's original broad application and generality.

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References (33)
  1. Do as i can, not as i say: Grounding language in robotic affordances. In Conference on Robot Learning, CoRL, 2022.
  2. Multi-level compositional reasoning for interactive instruction following. In Brian Williams, Yiling Chen, and Jennifer Neville (eds.), Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI, pp.  223–231, 2023.
  3. A persistent spatial semantic representation for high-level natural language instruction execution. In Conference on Robot Learning, volume 164 of Proceedings of Machine Learning Research, pp.  706–717, 2021.
  4. Answer set programming at a glance. Communications of the ACM, 54(12):92–103, 2011.
  5. RT-2: vision-language-action models transfer web knowledge to robotic control. CoRR, abs/2307.15818, 2023a.
  6. RT-1: robotics transformer for real-world control at scale. In Robotics: Science and Systems, 2023b.
  7. Search-based planning for manipulation with motion primitives. In 2010 IEEE International Conference on Robotics and Automation, May 2010.
  8. Task-motion planning for safe and efficient urban driving. In IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, pp.  2119–2125, 2020.
  9. Task and motion planning with large language models for object rearrangement. arXiv preprint arXiv:2303.06247, 2023.
  10. Pddlstream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning. In J. Christopher Beck, Olivier Buffet, Jörg Hoffmann, Erez Karpas, and Shirin Sohrabi (eds.), Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling, pp.  440–448, 2020.
  11. Instruct2act: Mapping multi-modality instructions to robotic actions with large language model. arXiv preprint arXiv:2305.11176, 2023.
  12. Language models as zero-shot planners: Extracting actionable knowledge for embodied agents. In International Conference on Machine Learning, pp.  9118–9147. PMLR, 2022.
  13. Prompter: Utilizing large language model prompting for a data efficient embodied instruction following. arXiv preprint arXiv:2211.03267, 2022.
  14. Code as policies: Language model programs for embodied control. In IEEE International Conference on Robotics and Automation, ICRA, pp.  9493–9500, 2023.
  15. Text2motion: From natural language instructions to feasible plans. arXiv preprint arXiv:2303.12153, 2023.
  16. LEBP - language expectation & binding policy: A two-stream framework for embodied vision-and-language interaction task learning agents. CoRR, abs/2203.04637, 2022a.
  17. A planning based neural-symbolic approach for embodied instruction following. Interactions, 9(8):17, 2022b.
  18. Film: Following instructions in language with modular methods. In International Conference on Learning Representations, 2022.
  19. Following natural language instructions for household tasks with landmark guided search and reinforced pose adjustment. IEEE Robotics Autom. Lett., 7(3):6870–6877, 2022.
  20. Episodic transformer for vision-and-language navigation. In 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021, pp.  15922–15932. IEEE, 2021.
  21. Generalized planning as heuristic search. International Conference on Automated Planning and Scheduling,International Conference on Automated Planning and Scheduling, 2021.
  22. Alfred: A benchmark for interpreting grounded instructions for everyday tasks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.  10740–10749, 2020.
  23. Progprompt: Generating situated robot task plans using large language models. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pp.  11523–11530. IEEE, 2023.
  24. One step at a time: Long-horizon vision-and-language navigation with milestones. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  15482–15491, 2022.
  25. Llm-planner: Few-shot grounded planning for embodied agents with large language models. arXiv preprint arXiv:2212.04088, 2023.
  26. Llama: Open and efficient foundation language models. CoRR, abs/2302.13971, 2023a.
  27. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023b.
  28. Chatgpt for robotics: Design principles and model abilities. Microsoft Auton. Syst. Robot. Res, 2:20, 2023.
  29. Self-instruct: Aligning language models with self-generated instructions. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023, pp.  13484–13508, 2023.
  30. Grounding open-domain instructions to automate web support tasks. In Proceedings of the 2021 Conference of the North American Chapter of, 2021.
  31. Mm-react: Prompting chatgpt for multimodal reasoning and action. arXiv preprint arXiv:2303.11381, 2023.
  32. Socratic models: Composing zero-shot multimodal reasoning with language. In The Eleventh International Conference on Learning Representations, ICLR, 2023.
  33. Fast segment anything, 2023.

Summary

  • The paper introduces a novel RoboGPT agent that integrates LLM planning with a 67,000-sample robotic dataset to enhance long-term decision-making in daily tasks.
  • It implements specialized modules for decomposing instructions into sub-goals and dynamically re-plans using real-time environmental feedback.
  • Experimental results show superior performance on the ALFRED benchmark and unseen tasks, achieving a notable 78% high-level planning accuracy.

RoboGPT: An Intelligent Agent for Embodied Long-Term Decisions in Daily Instruction Tasks

The paper "RoboGPT: an intelligent agent of making embodied long-term decisions for daily instruction tasks" presents an innovative approach to enhancing robotic planning capabilities using LLMs. The authors propose the RoboGPT agent, which integrates LLM-based planning combined with domain-specific knowledge from a newly created robotic dataset, to address the challenges in executing long-term sequential tasks based on natural language instructions. The research insights primarily focus on overcoming the inherent limitations of LLMs in robot planning through the introduction of specialized modules and fine-tuning methods.

Introduction

Robotic agents tasked with performing daily activities through natural language instructions must exhibit a deep understanding of common sense and long-term planning. The advancements in LLMs have facilitated significant progress in natural language processing, making them suitable candidates for complex robot planning tasks. However, despite their potent generalization abilities, LLMs sometimes generate plans that are not feasible for robotic execution. This paper addresses these issues by enhancing LLMs with domain-specific knowledge and implementing a flexible re-planning mechanism, thus ensuring logical validity and adaptability.

Key Contributions

The RoboGPT agent is comprised of two primary modules:

  1. LLMs-Based Planning: This module breaks down a task into several manageable sub-goals, with each sub-goal specifically designed to enhance the robot's navigation and manipulation abilities.
  2. RoboSkill Module: Designed to learn and execute skills particular to each sub-goal, this module ensures efficient task completion through improved navigation and manipulation.

The authors introduced a novel robotic dataset consisting of 67,000 samples from daily instruction tasks. This dataset allows the fine-tuning of the Llama model, enhancing its domain-specific planning capabilities. The newly developed dataset addresses nomenclature diversity and provides RoboGPT the ability to generalize to unseen tasks.

System Overview

The system architecture of RoboGPT is depicted in Figure 1 of the paper. The agent begins by decomposing high-level instructions into sub-goals using the RoboGPT planner. It then sequentially executes these sub-goals using the RoboSkill module, which integrates advanced navigation and interaction capabilities. A critical feature of the system is the Re-Plan module, which dynamically adjusts plans based on environmental feedback, thus addressing nomenclature diversity and ensuring task adaptability.

Experimental Results

The effectiveness of RoboGPT is demonstrated through empirical evaluations on the ALFRED benchmark and a custom-generated task set. The experimental metrics include success rate (SR), goal-condition success (GC), and high-level planning accuracy (HLP ACC).

  • Performance on ALFRED Tasks: RoboGPT achieves a significant improvement in performance over state-of-the-art (SOTA) methods, particularly in unseen tasks, indicating superior generalization and task planning rationality. Specifically, RoboGPT outperforms Prompter and LLM-Planner in SR and HLP ACC.
  • Generalization Tasks: RoboGPT exhibited robust performance in handling unseen and complex tasks, achieving a notable 78% HLP ACC – a significant leap compared to existing SOTA models like ChatGPT.

Implications and Future Directions

The research showcases the potential of integrating LLMs with domain-specific knowledge for embodied AI applications. The implications extend to practical implementations of robotic systems capable of performing a wide array of daily tasks with minimal human intervention. The theoretical advancements set a precedent for further exploration of LLM fine-tuning using domain-specific data, which can enhance the planning and execution capabilities of robotic agents.

Future developments should focus on:

  • Multi-modal Integration: Enhancing the agent's ability to process and integrate multi-modal inputs (visual, auditory, and textual) to improve task comprehension and execution.
  • Advanced Robotics Manipulation: Refining the manipulation algorithms to handle more complex and nuanced tasks, further bridging the gap between human and robot task execution efficiency.
  • Real-world Applications: Testing and implementing the RoboGPT system in real-world environments to validate the robustness and adaptability of the proposed methodologies outside controlled experimental settings.

Conclusion

The RoboGPT agent represents a significant advancement in the domain of robotic planning and execution. By fine-tuning LLMs with a comprehensive robotic dataset and implementing a robust re-planning mechanism, the authors have demonstrated a practical and effective solution for embodied long-term decision-making. The research offers a well-founded approach that could significantly enhance the capabilities of future robotic systems in daily instruction tasks, paving the way for more intelligent and autonomous robots.

This summary provides a detailed overview of the core contributions, experimental validation, and potential implications of the "RoboGPT" paper, catering to the interests and expertise of experienced researchers in the field.

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