Enabling Generic Robot Skill Implementation Using Object Oriented Programming

• URL: http://arxiv.org/abs/2508.10497v1
• Date: Thu, 14 Aug 2025 09:53:41 GMT
• Title: Enabling Generic Robot Skill Implementation Using Object Oriented Programming
• Authors: Abdullah Farrukh, Achim Wagner, Martin Ruskowski,
• Abstract summary: We propose a software framework that reduces the effort required to deploy a working robotic system.<n>The focus is solely on providing a concept for simplifying the different interfaces of a modern robot system.<n>The target system is a bin-picking cell containing a Yaskawa Motoman GP4.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Developing robotic algorithms and integrating a robotic subsystem into a larger system can be a difficult task. Particularly in small and medium-sized enterprises (SMEs) where robotics expertise is lacking, implementing, maintaining and developing robotic systems can be a challenge. As a result, many companies rely on external expertise through system integrators, which, in some cases, can lead to vendor lock-in and external dependency. In the academic research on intelligent manufacturing systems, robots play a critical role in the design of robust autonomous systems. Similar challenges are faced by researchers who want to use robotic systems as a component in a larger smart system, without having to deal with the complexity and vastness of the robot interfaces in detail. In this paper, we propose a software framework that reduces the effort required to deploy a working robotic system. The focus is solely on providing a concept for simplifying the different interfaces of a modern robot system and using an abstraction layer for different manufacturers and models. The Python programming language is used to implement a prototype of the concept. The target system is a bin-picking cell containing a Yaskawa Motoman GP4.

Related papers

• INGRID: Intelligent Generative Robotic Design Using Large Language Models [5.3909333359654275]
  INGRID (Intelligent Generative Robotic Design) is a framework that enables the automated design of parallel robotic mechanisms.<n>We decompose the design challenge into four progressive tasks: constraint analysis, kinematic joint generation, chain construction, and complete mechanism design.<n>By bridging the gap between mechanism theory and machine learning, INGRID enables researchers without specialized robotics training to create custom parallel mechanisms.
  arXiv  Detail & Related papers  (2025-09-04T03:08:01Z)
• A roadmap for AI in robotics [55.87087746398059]
  We are witnessing growing excitement in robotics at the prospect of leveraging the potential of AI to tackle some of the outstanding barriers to the full deployment of robots in our daily lives.<n>This article offers an assessment of what AI for robotics has achieved since the 1990s and proposes a short- and medium-term research roadmap listing challenges and promises.
  arXiv  Detail & Related papers  (2025-07-26T15:18:28Z)
• $π_0$: 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)
• Controlling diverse robots by inferring Jacobian fields with deep networks [48.279199537720714]
  Mirroring the complex structures and diverse functions of natural organisms is a long-standing challenge in robotics.<n>We introduce a method that uses deep neural networks to map a video stream of a robot to its visuomotor Jacobian field.<n>Our approach achieves accurate closed-loop control and recovers the causal dynamic structure of each robot.
  arXiv  Detail & Related papers  (2024-07-11T17:55:49Z)
• RoboCodeX: Multimodal Code Generation for Robotic Behavior Synthesis [102.1876259853457]
  We propose a tree-structured multimodal code generation framework for generalized robotic behavior synthesis, termed RoboCodeX. RoboCodeX decomposes high-level human instructions into multiple object-centric manipulation units consisting of physical preferences such as affordance and safety constraints. To further enhance the capability to map conceptual and perceptual understanding into control commands, a specialized multimodal reasoning dataset is collected for pre-training and an iterative self-updating methodology is introduced for supervised fine-tuning.
  arXiv  Detail & Related papers  (2024-02-25T15:31:43Z)
• 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)
• GRID: A Platform for General Robot Intelligence Development [22.031523876249484]
  We present a new platform for General Robot Intelligence Development (GRID) The platform enables robots to learn, compose and adapt skills to their physical capabilities, environmental constraints and goals. GRID is designed from the ground up to accommodate new types of robots, vehicles, hardware platforms and software protocols.
  arXiv  Detail & Related papers  (2023-10-02T04:09:27Z)
• 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)

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.