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Large Language Models for Robotics: Opportunities, Challenges, and Perspectives
Jiaqi Wang, Zihao Wu, Yiwei Li, Hanqi Jiang, Peng Shu, Enze Shi, Huawen Hu, Chong Ma, Yiheng Liu, Xuhui Wang, Yincheng Yao, Xuan Liu, Huaqin Zhao, Zhengliang Liu, Haixing Dai, Lin Zhao, Bao Ge, Xiang Li, Tianming Liu, Shu Zhang
TL;DR
Robotic applications of LLMs face fragmented interaction data and the difficulty of connecting text-based reasoning with embodied environments. This paper surveys LLM integration across robotics and evaluates a GPT-4V framework that combines language instructions with visual perceptions for task planning. Across diverse datasets, GPT-4V generates action plans for manipulation tasks, while limitations include homogeneous plans, prompt dependence, predefined actions, and closed-source constraints.
Problem
Robotics lacks broadly diverse interaction datasets, and text-only LLMs have limited compatibility with robotic visual perception in embodied tasks.
Method
The paper surveys LLMs in robotic tasks and develops a GPT-4V embodied task-planning framework combining natural-language instructions with visual perceptions.
Results
GPT-4V effectively uses natural-language instructions and visual perceptions to generate detailed action plans across over 30 cases in 9 datasets.
Takeaways & Limitations
The findings suggest that multimodal LLMs can serve as robotic brains for embodied intelligence and improve robots’ comprehension of instructions, environments, and actions.
Takeaways & Limitations
The framework produces homogeneous plans, depends on lengthy expert-crafted prompts, restricts robots to predefined actions, and uses a closed-source model.
Abstract
from arXiv · showhide
Large language models (LLMs) have undergone significant expansion and have been increasingly integrated across various domains. Notably, in the realm of robot task planning, LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions. However, for embodied tasks, where robots interact with complex environments, text-only LLMs often face challenges due to a lack of compatibility with robotic visual perception. This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks. Additionally, we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions. Our results, based on diverse datasets, indicate that GPT-4V effectively enhances robot performance in embodied tasks. This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights toward bridging the gap in Human-Robot-Environment interaction.
I. INTRODUCTION
LLMs offer strong language understanding and reasoning for robotics, but integrating them with embodied environments remains difficult because robotic interaction datasets are diverse and fragmented. The paper surveys LLM-based robotics and evaluates multimodal GPT-4V for embodied task planning.
- LLMs can translate natural-language instructions into machine-understandable actions, making robots more adaptive and flexible.
- Robotics datasets are difficult to collect at scale and often focus on single environments, objects, or task domains.
- The review addresses how robotics can harness LLM advances while overcoming challenges posed by robotic technology.
- The paper surveys LLM robotics literature across planning, manipulation, and reasoning.
- It examines technical approaches for generalized robot strategies and provides a foundational survey for researchers.
- The study assesses multimodal GPT-4V for robot task planning across diverse environments and scenarios.
II. RELATED WORK
Related work extends LLM robotics beyond text processing toward multimodal analysis, environmental adaptation, and more context-aware interaction with the physical world.
- Vision-language models have improved task guidance within broader advances in natural-language-based robotic intelligence.
- LLM-guided instruction and interaction methods can support more precise control, environmental adaptation, and personalized human-machine experiences.
- LLM robotics research combines textual, visual, and auditory inputs for more holistic task analysis.
- Multimodal task-planning systems aim to interpret and synthesize multiple data streams into actionable intelligence.
- Inner Monologue and SayCan exemplify progress toward dynamic, context-aware, and autonomous real-world interaction.
1) Natural Language Understanding :
Natural-language understanding enables LLM-based robots to interpret instructions, reason over complex tasks, and generate executable action sequences, with multimodal and interactive systems extending these capabilities.
- LLMs translate natural-language instructions into executable robot action sequences, with visual information significantly enhancing performance.
- Text2Motion addresses dependencies in skill sequences through geometrically feasible planning for complex sequential tasks.
- LLM-based robotic systems support logical reasoning, decision-making, and interactive learning beyond text generation.
- LM-Nav integrates language, vision, and action models for navigation while reducing reliance on costly trajectory annotation.
- ReAct uses working memory and abstract high-level goals, while KnowNo provides statistical completion guarantees for multistep planning.
- Pre-trained language models are increasingly applied to complex tasks aligned with real-world scenarios through self-supervision and task-specific adaptation.
- AGI language models can help robots generalize task planning to previously unseen environments and tasks.
- ChatGPT has been evaluated on logical, geometric, mathematical, navigation, manipulation, and embodied-agent control tasks.
B. Manipulation
LLM-based manipulation research uses semantic understanding, multimodal cues, and pretrained representations to improve planning, learning efficiency, and adaptation across tasks and environments.
- LLM-GROP extracts semantic information for commonsense object placement within multistep task-and-motion planning.
- VIMA converts robot manipulation tasks into unified sequence modeling with multimodal tasks and system-generalization evaluation.
- Pretrained visual-language representations can improve the sample efficiency of exploratory robot-learning methods.
- The surveyed studies emphasize model generalization across domains while differing in semantic extraction, multimodal learning, and task-specific optimization.
- TEXT2REWARD generates dense interactive reward codes, while InstructRL trains agents to interpret and act on human language instructions.
- These approaches use language modeling to improve human-robot interaction, adaptability, and flexibility across diverse tasks and environments.
3) Modular Approaches:
Modular approaches combine LLMs with specialized learning, perception, planning, or control components to improve flexibility, adaptation, and generalization across robotic tasks and environments.
- Modular Approaches: PROGRAMPORT translates linguistic concepts into programming elements through a modular framework for robot manipulation.Its neural modules learn general capabilities while supporting language-driven execution.
- Modular Approaches: LLMs can accelerate adaptation to unfamiliar tools by generating geometric shapes, descriptive tool models, and vector representations.This integration of linguistic information and meta-learning is reported to improve adaptation performance.
- Modular Approaches: Combining NLMap with SayCan provides a more flexible scene representation for long-term planning in open-world scenarios.NLMap contributes visual-language understanding of the environment to LLM-based planning.
- Modular Approaches: “Scaling Up and Distilling Down” combines LLMs, sampling-based planners, and policy learning to produce visual-linguistic motion strategies.The resulting multi-task strategy is reported to retain long-term behavior and robust manipulation skills outside the training distribution.
- Modular Approaches: MetaMorph uses a Transformer-based controller pretrained across robot morphologies to generalize to new morphologies and tasks.Robot morphology is represented as a Transformer output within a broad modular design space.
- Modular Approaches: Across these studies, modular LLM-based systems target rapid generalization and adaptation through language, vision, or both.Applications include tool adaptation, long-term planning, and polymorphic robot control, while traditional robotics burdens are contrasted with LLM-enabled flexibility.
2) Complex Task Reasoning and Decision-making:
LLMs support complex robotic reasoning by supplying semantic knowledge, guiding planning, translating multimodal instructions, explaining failures, and leveraging multimodal pretraining. Effectiveness remains dependent on the task, data, and training approach.
- Complex Task Reasoning and Decision-making: SayCan combines LLM knowledge with reinforcement learning, using skill value functions and textual skill labels for semantic task guidance.The LLM provides overarching semantic guidance while reinforcement learning identifies skill values.
- Complex Task Reasoning and Decision-making: Instruct2Act translates multimodal commands into robot action sequences using LLM-generated policy code that calls visual base models.This design provides visual comprehension of the task set through API calls.
- Complex Task Reasoning and Decision-making: LLM outputs can guide heuristic search planners in self-planning and PDDL planning.The reported role is guidance of planning search rather than direct execution of every action.
- Complex Task Reasoning and Decision-making: REFLECT queries an LLM with hierarchical summaries of multisensory robot experiences to reason about failures and guide corrective planning.The failure explanation is used by a language-based planner to complete the task successfully.
- Complex Task Reasoning and Decision-making: Visual-Linguistic-Action models combine vision-language pretraining with robot data, with internet-pretrained models reported to generate efficient strategies and generalize better.The passage reports models with up to 55 billion parameters.
- Complex Task Reasoning and Decision-making: Across these approaches, LLMs automate reasoning and decision-making by providing high-level semantic knowledge, sometimes integrated with vision and action for unseen tasks.Reported techniques include reinforcement learning, heuristic search, multimodal pretraining, and structured multi-model dialogue.
- Complex Task Reasoning and Decision-making: The effectiveness of integrated reasoning methods depends strongly on the task, data, and model-training approach, requiring context-specific selection.The passage explicitly cautions against treating one technique as uniformly suitable across settings.
3) Interactive Strategies:
Interactive strategies fuse LLMs with multimodal perception and memory to support richer environmental understanding and human-like interaction. The paper frames this convergence as promising but technically and socially challenging.
- Interactive Strategies: Matcha uses LLMs to enhance interactive multimodal perception across inputs such as visual and auditory data.It proposes an augmented multimodal interactive agent.
- Interactive Strategies: Generative agents use LLMs to store, synthesize, and apply memories for plausible human-like behaviors and interaction patterns.Their architecture is designed to simulate human behavior through memory-informed generation.
- Interactive Strategies: Interactive LLM strategies emphasize fusion with image-recognition and speech-processing systems to augment human-like cognitive and processing abilities.The passage identifies intelligent assistants, robotics, and augmented reality as relevant application domains.
- Interactive Strategies: Combining multimodal perception with simulated human thought and action is presented as a route toward more versatile systems interacting with humans at greater complexity.The same convergence raises technical, ethical, and social adaptation challenges.
- Interactive Strategies: The study develops a GPT-4V embodied task-planning framework and evaluates it using datasets, prompt design, and experimental results.The framework is based on multimodal embodied-task planning.
A. Datasets
The evaluation tests GPT-4V task planning on manipulation and grasping cases from nine datasets, using video demonstrations and instructions as inputs. Plans are structured through multimodal prompting, action selection, and comparison with ground-truth demonstrations.
- A. Datasets: Over 40 manipulation and grasping cases from nine datasets evaluate GPT-4V using video demonstrations and natural-language instructions.The cases span scenarios including kitchen pickups and tabletop rearrangements and are drawn from the Google Open X-Embodiment Dataset.
- A. Datasets: The multimodal prompt adds images to a prior text prompt and structures GPT-4V queries around five components.These components cover system role, action pool, example output, environment image with instruction, and evaluation.
- A. Datasets: GPT-4V evaluates generated plans against ground-truth video demonstrations by scoring alignment and providing an explanation.The evaluation component is included in the prompt design.
- A. Datasets: The experimental pipeline generates step-by-step instructions, selects actions and objects from a predefined pool, and quantitatively compares plans with ground-truth instructions.Testing was conducted on nine publicly available robot datasets.
- A. Datasets: The figures present generated task plans across RT-1 Robot Action, QT-Opt, Berkeley Bridge, Freiburg Franka Play, and USC Jaco Play.These datasets occupy the top, middle, and bottom panels of Fig. 2.
- A. Datasets: In RT-1 Robot Action, GPT-4V identified the target, decomposed the task into four operations, and represented actions such as moving the hand to the bottom drawer.The generated plan also tracks changing spatial relationships through an environment state and receives a matching score.
- A. Datasets: In QT-Opt, GPT-4V generated generalized grasping instructions for the vague command “pick anything” while remaining consistent with the ground truth.The test specifically examines semantically vague instructions and complex scenes.
- A. Datasets: Across nine diverse datasets, GPT-4V’s self-evaluated average matching scores indicate consistently high agreement between generated plans and ground-truth demonstrations.Additional generated plans are shown in Fig. 3 for Berkeley Autolab UR5, NYU VINN, BC-Z, and TOTO Benchmark.
VI. LIMITATION, DISCUSSION AND FUTURE WORK
GPT-4V shows promise for multimodal robotic task planning, but current systems remain constrained by plan homogeneity, prompting demands, predefined actions, and deployment barriers. Future applications in agriculture, healthcare, and brain-computer interfaces require stronger perception and reasoning.
- Limitations: GPT-4V can support robotic task planning through multimodal reasoning and vision-language understanding, but generated plans remain homogeneous and insufficiently embodied for complex environments.The paper identifies a need for more detailed, specific, and robust designs.
- Limitations: Reliable multimodal outputs require carefully crafted, lengthy prompts that demand domain expertise and extensive prompting techniques.
- Limitations: Predefined robot actions constrain executional freedom and robustness, while GPT-4V’s closed-source API and delays hinder embedded and real-time applications.
- Future Work: Multimodal LLM-centric robots could support labor-intensive precision-agriculture tasks such as harvesting.
- Future Work: Robot-assisted screening and surgery impose greater perceptual and reasoning demands because healthcare tasks require safety, precision, and individual tailoring.
- Future Work: Aligning brain signals with natural language through contrastive models such as CLIP suggests a pathway toward LLM-centric robotic brain-computer interfaces.
VII. CONCLUSION
The paper surveys LLM integration across robotic systems and finds that reasoning, language understanding, and multimodal processing can improve robots’ comprehension. Evaluation across embodied-planning cases and datasets supports GPT-4V’s ability to combine instructions with visual perception, while transparency, robustness, safety, and real-world applicability remain unresolved.
- Conclusion: LLMs’ reasoning, language understanding, and multimodal processing abilities can enhance robots’ comprehension of instructions, environments, and required actions.
- Conclusion: GPT-4V was evaluated on over 30 cases across 9 datasets for embodied task planning.
- Conclusion: GPT-4V can combine natural language instructions and visual perceptions to generate detailed action plans for manipulation tasks.
- Conclusion: Transparency, robustness, safety, and real-world applicability remain challenges for practical LLM-based AI systems.
- Future Perspective: Multimodal data and sim-to-real techniques could support developing and validating intelligent robots in simulation before deployment.
APPENDIX
The appendix presents the framework’s complete prompt and additional experimental results. The prompt instructs a robot task planner to convert natural-language instructions and environmental information into stepwise robot actions.
- The appendix includes the complete framework prompt and additional experimental results in Figures 4–7.
- The prompt directs a robot task planner to use natural-language instructions and working-environment information.
- The planner should decompose the instruction into step-by-step instructions and corresponding robot actions.
SYSTEM
The framework prompts GPT-4V to generate structured robot task plans from natural-language instructions and visual environments. It represents plans as stepwise instructions, predefined actions, target objects, and before-and-after environment states, then evaluates alignment with demonstrations.
- The framework records environment_before and environment_after states to represent how the planned actions change the scene.
- The generated plan separates task sequence, step instructions, and target object while restricting actions to a predefined action pool.
- GPT-4V receives a natural-language instruction and an image of the working environment, then returns a JSON task-planning response.
- In one example, GPT-4V plans moving a banana from a white bowl, formalizing the steps as move_hand, grasp_object, and move_hand actions.
- In another example, the model moves a brown chip bag from a drawer to the counter and achieves a 10/10 matching score.