Robo-Blocks: Generative Scaffolding in End-User Design and Programming of Social Robots
- URL: http://arxiv.org/abs/2605.28154v1
- Date: Wed, 27 May 2026 08:39:01 GMT
- Title: Robo-Blocks: Generative Scaffolding in End-User Design and Programming of Social Robots
- Abstract summary: Large language models (LLMs) can obscure critical elements of programming and supplant designer intent.<n>We show how Robo-Blocks, a block-based programming environment, offers novice robot programmers generative scaffolding through structured narratives.<n>We present design insights for the effective use of generative scaffolding and its integration into the practice of social-robot programming.
- Score: 32.50504092559475
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Programming social robots is challenging for novice robot programmers due to required expertise in planning, interaction design, and programming. While large language models (LLMs) hold significant promise through code generation from natural-language descriptions, they can obscure critical elements of programming and supplant designer intent, eventually resulting in over-reliance instead of developing programming skills. In this paper, we explore how LLM-based social-robot-programming tools can support novice robot programmers through a Research through Design (RtD) process. We designed and prototyped Robo-Blocks, a block-based programming environment that leverages LLMs to offer novice robot programmers generative scaffolding through structured narratives that connect high-level ideas to executable robot behaviors. Through deployment with novices, we discovered emerging user personas and usage patterns for generative scaffolding and showed how this scaffolding shapes end-user design and programming strategies. We present design insights for the effective use of generative scaffolding and its integration into the practice of social-robot programming.
Related papers
- APPROVE: Visual End-User-in-the-Loop Robot Programming with LLMs [0.0]
We present APPROVE (AI-Powered Programming for Robots with Visual End-User Feedback), an LLM-based multi-modal end-user programming framework.<n> Generated programs are visualized using a block-based interface in Blockly, allowing users to confirm, modify, or reject them before execution.<n>Our approach contributes a human-centered design for LLM-based robot programming that emphasizes user trust, intent alignment, and reusability.
arXiv Detail & Related papers (2026-08-19T08:22:13Z) - RoboCritics: Enabling Reliable End-to-End LLM Robot Programming through Expert-Informed Critics [25.430531368529685]
Large Language Models (LLMs) hold the potential to lower the barrier to robot programming by enabling task specification through natural language.<n>We present RoboCritics, an approach that augments LLM-based robot programming with expert-informed motion-level critics.
arXiv Detail & Related papers (2026-03-06T20:02:53Z) - $π_0$: A Vision-Language-Action Flow Model for General Robot Control [77.32743739202543]
We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge.
We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people, and its ability to acquire new skills via fine-tuning.
arXiv Detail & Related papers (2024-10-31T17:22:30Z) - RoboScript: Code Generation for Free-Form Manipulation Tasks across Real
and Simulation [77.41969287400977]
This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation.
We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language.
We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
arXiv Detail & Related papers (2024-02-22T15:12:00Z) - Automatic Robotic Development through Collaborative Framework by Large
Language Models [13.957351735394683]
We propose an innovative automated collaboration framework inspired by real-world robot developers.
This framework employs multiple LLMs in distinct roles analysts, programmers, and testers.
Analysts delve deep into user requirements, enabling programmers to produce precise code, while testers fine-tune the parameters.
arXiv Detail & Related papers (2024-02-06T04:40:27Z) - Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis [82.59451639072073]
General-purpose robots operate seamlessly in any environment, with any object, and utilize various skills to complete diverse tasks.
As a community, we have been constraining most robotic systems by designing them for specific tasks, training them on specific datasets, and deploying them within specific environments.
Motivated by the impressive open-set performance and content generation capabilities of web-scale, large-capacity pre-trained models, we devote this survey to exploring how foundation models can be applied to general-purpose robotics.
arXiv Detail & Related papers (2023-12-14T10:02:55Z) - RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation [68.70755196744533]
RoboGen is a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation.
Our work attempts to extract the extensive and versatile knowledge embedded in large-scale models and transfer them to the field of robotics.
arXiv Detail & Related papers (2023-11-02T17:59:21Z) - Logic programming for deliberative robotic task planning [2.610470075814367]
We present a survey on recent advances in the application of logic programming to the problem of task planning.
We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation.
arXiv Detail & Related papers (2023-01-18T14:11:55Z) - Tiny Robot Learning: Challenges and Directions for Machine Learning in
Resource-Constrained Robots [57.27442333662654]
Machine learning (ML) has become a pervasive tool across computing systems.
Tiny robot learning is the deployment of ML on resource-constrained low-cost autonomous robots.
Tiny robot learning is subject to challenges from size, weight, area, and power (SWAP) constraints.
This paper gives a brief survey of the tiny robot learning space, elaborates on key challenges, and proposes promising opportunities for future work in ML system design.
arXiv Detail & Related papers (2022-05-11T19:36:15Z) - iRoPro: An interactive Robot Programming Framework [2.7651063843287718]
iRoPro allows users with little to no technical background to teach a robot new reusable actions.
We implement iRoPro as an end-to-end system on a Baxter Research Robot.
arXiv Detail & Related papers (2021-12-08T13:53:43Z) - A Reference Software Architecture for Social Robots [64.86618385090416]
We propose a series of principles that social robots may benefit from.
These principles lay also the foundations for the design of a reference software architecture for Social Robots.
arXiv Detail & Related papers (2020-07-09T17:03:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.