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ChatGPT for Robotics: Design Principles and Model Abilities

Sai Vemprala, Rogerio Bonatti, Arthur Bucker, Ashish Kapoor

arXiv:2306.17582v2cs.AIcs.CLcs.HCcs.LGcs.RO

TL;DR

The paper addresses how language models can support robotics despite the need for physical-world reasoning and safe action execution. It combines prompt-engineering strategies with a high-level function library and evaluates ChatGPT across diverse simulated and real-world tasks. The study finds that ChatGPT can solve several robotics tasks in zero-shot settings while supporting natural-language interaction and closed-loop reasoning, but human supervision remains necessary for safety.

  • Problem

    Robotics requires physical-world understanding and action execution, while existing language-based approaches are often rigid, narrow in scope, or open-loop.

  • Method

    The paper combines prompt-engineering techniques with a high-level function library that maps ChatGPT’s intent-derived function calls to robot-platform APIs.

  • Results

    ChatGPT solves various robotics-related tasks zero-shot, adapts to multiple form factors, and supports closed-loop reasoning through conversation.

  • Takeaways & Limitations

    The framework supports natural-language robotics interaction across tasks ranging from basic reasoning to aerial robotics, manipulation, and visual navigation.

  • Takeaways & Limitations

    Human supervision and testing remain necessary because ChatGPT can generate incorrect responses and unexpected behaviors, especially in physical robotics deployments.

Abstract

from arXiv · show

This paper presents an experimental study regarding the use of OpenAI's ChatGPT for robotics applications. We outline a strategy that combines design principles for prompt engineering and the creation of a high-level function library which allows ChatGPT to adapt to different robotics tasks, simulators, and form factors. We focus our evaluations on the effectiveness of different prompt engineering techniques and dialog strategies towards the execution of various types of robotics tasks. We explore ChatGPT's ability to use free-form dialog, parse XML tags, and to synthesize code, in addition to the use of task-specific prompting functions and closed-loop reasoning through dialogues. Our study encompasses a range of tasks within the robotics domain, from basic logical, geometrical, and mathematical reasoning all the way to complex domains such as aerial navigation, manipulation, and embodied agents. We show that ChatGPT can be effective at solving several of such tasks, while allowing users to interact with it primarily via natural language instructions. In addition to these studies, we introduce an open-sourced research tool called PromptCraft, which contains a platform where researchers can collaboratively upload and vote on examples of good prompting schemes for robotics applications, as well as a sample robotics simulator with ChatGPT integration, making it easier for users to get started with using ChatGPT for robotics.

1. Introduction

The paper examines ChatGPT as a more versatile robotics interface for systems that must translate language into physically possible actions. It proposes a high-level function library and prompting strategies, then evaluates dialogue, code generation, XML tags, and closed-loop reasoning across robotics tasks.

  • Motivation: Robotics systems must combine language understanding with physical-world reasoning and executable action sequences.They require understanding physics and environmental context beyond the original scope of language models.
  • Motivation: Existing language-based robotics approaches often target narrow scenarios or operate open-loop without fluid user feedback and behavior correction.The cited approaches include navigation, human-robot interaction, and manipulation applications.
  • Approach: The proposed approach uses a high-level function library that maps descriptive calls to platform APIs, allowing ChatGPT to parse user intent and chain functions through natural dialogue.The library avoids requiring platform-specific code output or extensive fine-tuning for each platform.
  • Findings: The study reports zero-shot task solving across multiple form factors while enabling behavior correction through conversation.The evaluation includes both simulation and real-world experiments resulting from ChatGPT’s plans.
  • Approach: The robotics pipeline combines free-form dialogue, code prompting, XML tags, closed-loop reasoning, and high-level functions to generate robotics solutions.The paper evaluates these techniques across mathematical, logical, geometrical, embodied-agent, aerial-navigation, and manipulation scenarios.
  • Resources: PromptCraft provides a collaborative platform for sharing prompting strategies, alongside an AirSim-ChatGPT simulator for exploring robotics scenarios.The simulator includes a sample drone-navigation environment.

2. Robotics with ChatGPT

The paper proposes a prompting pipeline that combines descriptive high-level robot APIs, task and context specifications, user feedback, and structured outputs to apply ChatGPT to robotics. The approach supports fixed APIs, model-created behaviors, iterative correction, and machine-readable responses.

  • The pipeline defines the robot library, prompts ChatGPT with objectives, allowed functions, constraints, and response requirements, keeps a user in the loop for evaluation, and deploys iterated code.Evaluation can occur through direct analysis or simulation before deployment.
  • A high-level function library maps descriptive robot functions to executable platform implementations, giving ChatGPT an API vocabulary for reasoning and execution.The functions can wrap existing perception and action libraries, such as object detection, mapping, planning, controls, and grasping.
  • 2.2. Clear description of the task details in the prompt: Clear API names, inputs, outputs, task objectives, constraints, environment, and current state help ChatGPT reason about executable task sequences.The paper recommends documenting APIs and, when needed, decomposing them into sub-components with clear inputs and outputs.
  • 2.3. Special arguments to bias the answer’s structure: ChatGPT can create new high-level concepts, fuse existing APIs, and generate lower-level code when the fixed library lacks a needed behavior.Figure 4 presents new API development as an extension beyond the fixed API set illustrated in Figure 3.
  • Additional dialogue instructions can correct incorrect zero-shot outputs without rebuilding the original prompt from scratch.The paper describes interactive correction as a practical use of ChatGPT’s dialogue capability.
  • 2.3. Special arguments to bias the answer’s structure: XML tags and language-specific code requests constrain response structure for automatic parsing and downstream script or robot execution.The paper also describes list-pattern outputs as another way to impose a structured response format.

3. ChatGPT abilities for solving robotics problems

The experiments examine ChatGPT across robotics tasks ranging from zero-shot planning and visual reasoning to aerial navigation, manipulation, and closed-loop interaction. Results show useful code generation, natural-language control, and feedback-driven adaptation, while deployment still requires human oversight and simulator-based evaluation.

  • Practical physical deployment requires a human to monitor and intervene, with simulators recommended for evaluation before real-world use.
  • ChatGPT solves several robotics tasks zero-shot using only a prompt and high-level function-library description, without code examples.
  • In visual servoing, ChatGPT combines provided APIs, appearance reasoning, OpenCV functions, proportional control, and SVG-generated scene estimates to catch a basketball.
  • ChatGPT provides a natural-language interface for real-drone control, asks clarification questions, and generates circular and lawnmower inspection code from base APIs.
  • Closed-loop interaction supports feedback-driven correction and complex skill composition, but more research is needed to describe scene context for complex navigation environments.
  • In block manipulation, curriculum learning lets ChatGPT chain picking and placing skills into block arrangements, including a physically constructible Microsoft logo.
  • For drone obstacle avoidance, ChatGPT builds most algorithmic components but needs textual feedback about orientation, after which it makes localized code improvements.

4. PromptCraft, a collaborative tool for LLM + Robotics research

PromptCraft is introduced as a collaborative open-source resource for sharing and evaluating robotics prompt-engineering strategies. It combines categorized prompt examples, community ratings, and an AirSim environment for controlled prototyping.

  • PromptCraft addresses the lack of comprehensive, accessible robotics prompting resources by enabling researchers to share positive and negative interaction examples.
  • The Github-based platform organizes prompt-engineering examples across robotics categories including navigation, grasping, and manipulation.
  • Users can submit examples and rate others’ submissions, supporting a community-driven resource for researchers working with LLMs.
  • PromptCraft provides an AirSim environment with a ChatGPT wrapper for prototyping prompts and algorithms in a controlled simulated setting.
  • The platform aims to support prompt engineering as an empirical science and help researchers advance the field.

5. Related Work

Prior robotics-language systems support specific scenarios or predefined behaviors, but many remain rigid, limited in functionality, or open-loop. This work emphasizes conversational correction and a generalizable pipeline across robotics domains.

  • Language-based robotics applications span human-robot interaction, navigation, and manipulation, but often target specific form factors or scenarios.
  • SayCan ranks action types within a robot-specific library, whereas RT-1 learns language-to-low-level-action mappings without intermediate high-level functions.
  • ChatGPT’s conversational ability lets users interactively improve and correct robot behavior instead of regenerating a zero-shot answer after re-engineering the prompt.
  • The proposed work aims for a generalizable robotics pipeline and design principles across fields rather than a single domain such as manipulation or task planning.

6. Conclusions and Future Work

The paper presents a prompt-engineering framework for robotics built around high-level APIs and user-supervised code generation. It demonstrates broad potential while emphasizing human oversight, safety validation, and future closed-loop research.

  • The framework combines prompt-engineering principles with a high-level API library for generating robotics-control code.The APIs are designed to support adaptation across robotics applications and to allow generated code to be tested, verified, and validated by a human.
  • PromptCraft is introduced as a collaborative tool for sharing and voting on robotics prompting schemes, alongside a sample simulator with ChatGPT integration.The authors present it as a way to help researchers pursue further work in this area.
  • Human supervision remains necessary because the authors warn that LLMs can generate incorrect responses, especially in safety-critical robotics applications.They recommend testing, validation, and verification pipelines before code is executed on a robot.
  • Most examples use open perception-action loops without subsequent feedback to ChatGPT, leaving closed-loop control as an important direction for future research.The paper points toward textual or specialized modalities for supplying task feedback.

B. Original initial prompts for robotics tasks examples

The appendix presents initial prompts that constrain ChatGPT’s interaction with diverse robotic systems through explicit capabilities, command formats, and task-specific objectives. Examples span visual servoing, aerial simulation, navigation, and manipulation.

  • B.1. Spatial-temporal reasoning: catching a basketball with visual servoing: Visual servoing prompts frame basketball catching as planar XY positioning using a monocular camera and image-based ball detection.The robot is instructed to reach the ball’s landing location, with OpenCV available for detecting it as an orange blob.
  • B.2. Aerial robotics: real-world drone flight: Aerial-flight prompts require AirSim interactions to use explicit Question, Code, and Reason response tags.The example also supplies object locations and asks the model to produce executable commands with explanations.
  • B.3. Aerial robotics: AirSim industrial inspection: Industrial-inspection prompts use the same Question, Reason, and Code structure while requiring clarification when multiple target objects are present.The example asks the model to distinguish between two spheres before proceeding in a scene containing turbines, solar panels, and a car.
  • B.4. Aerial robotics: AirSim obstacle avoidance: Obstacle-avoidance prompts place a drone in a static AirSim scene and restrict responses to clarification, reasoning, or code commands.The prompt explicitly states that objects other than the drone are not movable.
  • B.5. Embodied agent: Habitat navigation: Habitat navigation prompts limit the embodied agent to turn(angle) and move(distance), using camera-depth observations represented as object, distance, and angle triples.The agent must return only one command at each step.
  • B.6. Embodied agent: AirSim object navigation: AirSim object-navigation prompts define an embodied agent’s available functions and require structured interaction through Question, Reason, and Code responses.The scene is described as containing several objects, with the model instructed to use only the listed functions.
  • B.7. Manipulation with curriculum learning: Picking, stacking, and building the Microsoft logo: Manipulation prompts describe a six-degree-of-freedom arm with a suction pump and provide grab, release, and position-query functions for task assistance.The setup supports commands conditioned on a scene and a specified manipulation task.

D.1. Computer vision problem

The computer-vision example asks ChatGPT to transform an RGBD image observation into world coordinates. The procedure uses the observed pixel, its perspective depth, camera intrinsics, and camera pose.

  • The problem supplies an image point P at pixel coordinates (x, y), its perspective depth, camera pose, and camera intrinsics.These values are accessed through get_depth(x, y), get_pose(), and get_intrinsics().
  • Camera intrinsics describe the projection from the 3D scene to the 2D image plane, while camera pose describes the camera’s position and orientation in the world.Both are required to recover the observed point’s world position.
  • The transformation first maps pixel coordinates into camera coordinates using the inverse intrinsics matrix and depth, then maps camera coordinates into world coordinates using the camera pose.This is the pipeline ChatGPT is asked to implement in Python.

D.2. Transformation matrices

The section addresses transforming coordinates across world, vehicle, and camera conventions. It composes the world-to-vehicle and vehicle-to-camera transformations, while accounting for differing coordinate conventions.

  • The example involves a world, robot car, and camera that each may use different coordinate conventions.The car and camera are represented as distinct coordinate frames relative to the world and vehicle.
  • The world-to-camera transformation is obtained by composing the world-to-vehicle and vehicle-to-camera transformation matrices.The matrices are multiplied as T_world_camera = T_world_vehicle T_vehicle_camera.
  • Implementing get_transformation_matrix(world, car) requires converting the car’s coordinate convention to match the world’s convention.The example specifies different axis directions and a car position of (5, 0, 1).

D.3. Balancing a ball on a plate

The section presents a closed-loop controller for balancing a ball at the center of a plate. It repeatedly observes the ball position, computes bounded pitch and roll corrections, and applies them through plate-control functions.

  • The controller continuously checks the ball’s image position and adjusts the plate when the ball is off-center.The image center is at (160, 120), based on the 320x240 resolution.
  • The controller clips pitch and roll commands to the allowed range [-0.1, 0.1].This prevents commands from exceeding the plate’s specified actuation limits.
  • Pitch and roll corrections are computed from the ball’s displacement from the image center.The adjustments use the vertical and horizontal pixel offsets from y = 120 and x = 160.
  • The loop waits briefly between corrections to reduce over-adjustment while repeatedly applying the control cycle.Each cycle reads the ball position, calculates corrections, sets both plate angles, and then pauses.
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