pith. the verified trust layer for science. sign in

arxiv: 2604.08787 · v1 · submitted 2026-04-09 · 💻 cs.RO

One Interface, Many Robots: Unified Real-Time Low-Level Motion Planning for Collaborative Arms

Yue Feng , Weicheng Huang , I-Ming Chen This is my paper

Pith reviewed 2026-05-10 16:44 UTC · model grok-4.3

classification 💻 cs.RO
keywords collaborative robotsmotion planningtrajectory generationpolynomial interpolationquadratic programmingunified interfacereal-time controlend-effector trajectories
0
0 comments X p. Extension

The pith

A single polynomial interpolator and quadratic programming solver unifies real-time motion planning across different collaborative robot arms.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper seeks to establish a common minimal interface for real-time low-level motion planning that works on collaborative robotic arms from heterogeneous hardware platforms. It combines an n-degree polynomial interpolator with a quadratic programming solver to produce smooth trajectories that specify exact position, velocity, and acceleration continuously. Validation occurs through three experiments: accurate offline drawing of geometric shapes, interruptible grasping of objects on a moving base, and teleoperation between two arms for dexterous tasks. A sympathetic reader would care because this promises that robot motion software can be written once and reused without hardware-specific rewrites or retuning.

Core claim

By employing an n-degree polynomial interpolator in conjunction with a quadratic programming solver, the proposed method generates smooth, continuously differentiable trajectories with precise position, velocity, and acceleration profiles for real-time end-effector trajectory control on collaborative arms.

What carries the argument

n-degree polynomial interpolator paired with a quadratic programming solver that computes constrained smooth trajectories while meeting real-time requirements.

If this is right

  • The trajectories remain continuously differentiable with controlled position, velocity, and acceleration.
  • The method supports accurate offline drawing of various geometric shapes.
  • Interruptible low-frequency re-planning succeeds for grasping dynamic objects on moving mobile robots.
  • Teleoperation between two arms reliably handles sequences of dexterous manipulations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach may shorten integration time when mixing robots from multiple vendors in one workspace.
  • It could be extended to mobile manipulators by adding base-motion constraints to the same solver.
  • Success in low-frequency re-planning suggests the interface might support adaptive tasks in changing environments without redesign.

Load-bearing premise

A single minimal interface based on polynomial interpolation and quadratic programming can abstract away hardware-specific differences in collaborative arms while maintaining real-time performance, safety, and precision without additional per-platform tuning.

What would settle it

Applying the interface to a new collaborative arm model and finding either failure to maintain real-time updates, violation of safety limits, or the necessity of platform-specific tuning to reach the reported precision and smoothness would disprove the claim.

Figures

Figures reproduced from arXiv: 2604.08787 by I-Ming Chen, Weicheng Huang, Yue Feng.

Figure 1
Figure 1. Figure 1: Motion Planning Visialization. A) Planned Cartesian motion of the robotic arm end-effector on the x–z plane through four waypoints, with uniform 2-second durations. Motion trails illustrate key snapshots. B) Online replanning triggered at the red triangle introduces three new waypoints. The arm smoothly transitions to the updated path and lands on the lower-left corner on time. C Yue Feng and I-Ming Chen a… view at source ↗
read the original abstract

This paper proposes a common interface for real-time low-level motion planning of collaborative robotic arms, aimed at enabling broader applicability and improved portability across heterogeneous hardware platforms. In previous work, we introduced WinGs Operating Studio (WOS), a middleware solution that abstracts diverse robotic components into uniform software resources and provides a broad suite of language-agnostic APIs. This paper specifically focuses on its minimal yet flexible interface for real-time end-effector trajectory control. By employing an n-degree polynomial interpolator in conjunction with a quadratic programming solver, the proposed method generates smooth, continuously differentiable trajectories with precise position, velocity, and acceleration profiles. We validate our approach in three distinct scenarios. First, in an offline demonstration, a collaborative arm accurately draws various geometric shapes on paper. Second, in an interruptible, low-frequency re-planning setting, a robotic manipulator grasps a dynamic object placed on a moving mobile robot. Finally, we conducted a teleoperation experiment in which one robotic arm controlled another to perform a series of dexterous manipulations, confirming the proposed method's reliability, versatility, and ease of use.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes a unified minimal interface for real-time low-level motion planning of collaborative robotic arms, implemented within the WinGs Operating Studio (WOS) middleware. It uses an n-degree polynomial interpolator paired with a quadratic programming solver to generate smooth, continuously differentiable trajectories with controlled position, velocity, and acceleration profiles. The approach is validated qualitatively in three scenarios: offline drawing of geometric shapes, interruptible re-planning for grasping a dynamic object on a mobile base, and teleoperation where one arm controls another for dexterous tasks.

Significance. If the central claim holds, the work would offer a practical contribution to robot middleware by reducing the need for platform-specific motion planners, building directly on the authors' prior WOS framework with standard tools (polynomial interpolation and QP). This could enhance portability across heterogeneous collaborative arms. However, the absence of quantitative metrics, timing data, or explicit multi-platform tests with no per-robot tuning limits the assessed significance; the method's generality remains unproven beyond the described qualitative demonstrations.

major comments (2)
  1. [Abstract] Abstract (validation scenarios): The three scenarios are presented only qualitatively with no reported quantitative metrics (e.g., trajectory tracking error, computation times, success rates), error bars, or baseline comparisons. This leaves the claims of smoothness, real-time performance, and versatility only moderately supported, as the central claim requires evidence that the polynomial+QP interface delivers precise profiles across use cases.
  2. [Abstract] Abstract (validation scenarios): The claim that a single minimal interface abstracts hardware-specific differences (kinematics, joint limits, dynamics) without per-platform tuning is not supported by the described experiments. No indication is given of testing on multiple distinct robot models, nor confirmation that the QP objective and constraints remain identical without robot-specific parameters (e.g., from URDF or scaling factors).
minor comments (2)
  1. [Abstract] The abstract refers to an 'n-degree polynomial' without specifying the value of n or the exact boundary conditions used in the interpolator; providing these details would improve reproducibility.
  2. The description of the QP solver lacks any mention of the objective function formulation or inequality constraints; clarifying whether these embed platform-dependent quantities would strengthen the 'minimal interface' argument.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments. We address each major comment point by point below, providing the strongest honest defense of the manuscript while noting planned revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract] Abstract (validation scenarios): The three scenarios are presented only qualitatively with no reported quantitative metrics (e.g., trajectory tracking error, computation times, success rates), error bars, or baseline comparisons. This leaves the claims of smoothness, real-time performance, and versatility only moderately supported, as the central claim requires evidence that the polynomial+QP interface delivers precise profiles across use cases.

    Authors: The validation is intentionally qualitative, as the core contribution is the design of a minimal unified interface within the WOS middleware rather than a performance benchmark. The offline drawing demonstrates accurate geometric reproduction via the polynomial trajectories, the dynamic grasping shows successful interruptible re-planning for a moving target, and the teleoperation experiment confirms reliable real-time control for dexterous tasks. These scenarios collectively illustrate smoothness, real-time capability, and versatility without per-task tuning. No baselines are included because the work introduces a new interface rather than comparing planners. We agree that computation times would add value and will incorporate any available timing data from the experimental logs in the revision. revision: partial

  2. Referee: [Abstract] Abstract (validation scenarios): The claim that a single minimal interface abstracts hardware-specific differences (kinematics, joint limits, dynamics) without per-platform tuning is not supported by the described experiments. No indication is given of testing on multiple distinct robot models, nor confirmation that the QP objective and constraints remain identical without robot-specific parameters (e.g., from URDF or scaling factors).

    Authors: The WOS middleware layer handles hardware abstraction (including kinematics, joint limits, and dynamics via standard mechanisms such as URDF), so the motion-planning interface itself remains minimal and identical across platforms. The n-degree polynomial interpolator and QP solver employ generic objectives and constraints on end-effector position, velocity, and acceleration; no robot-specific scaling or parameters are embedded in the QP formulation. The three experiments were executed on collaborative arms using this same interface without modification, demonstrating portability in practice. While the manuscript does not report explicit tests on multiple distinct robot models from different manufacturers, the middleware design ensures the interface requires no per-platform tuning. We will revise the abstract and introduction to more clearly attribute the abstraction to the WOS middleware and emphasize the generic nature of the QP constraints. revision: yes

Circularity Check

0 steps flagged

Minor self-citation to prior WOS middleware; trajectory generation uses standard polynomial+QP without reduction to fitted inputs or self-defined results

full rationale

The paper's core method applies an n-degree polynomial interpolator with a quadratic programming solver to produce smooth trajectories, which relies on well-established mathematical techniques rather than any derivation that reduces to quantities defined or fitted within this work. The only self-reference is to the authors' prior WOS middleware for the interface abstraction, but this is not load-bearing for the trajectory claims and does not create a chain where the central result is justified solely by unverified self-citation. No equations or validation steps in the provided text exhibit self-definitional loops, fitted inputs renamed as predictions, or ansatzes smuggled via citation. The approach remains self-contained against external benchmarks like standard polynomial interpolation and QP solvers.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard properties of polynomials for trajectory smoothness and the assumption that QP can run in real time on typical robotic hardware; no free parameters, new entities, or ad-hoc axioms are introduced in the abstract.

axioms (2)
  • standard math n-degree polynomials produce continuously differentiable trajectories with controllable position, velocity, and acceleration profiles.
    Basic mathematical property of polynomial functions invoked for the interpolator.
  • domain assumption Quadratic programming can be solved sufficiently fast to support real-time replanning on collaborative arm hardware.
    Assumes computational feasibility for the control loop without specifying hardware or timing bounds.

pith-pipeline@v0.9.0 · 5494 in / 1358 out tokens · 71547 ms · 2026-05-10T16:44:07.728977+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

36 extracted references · 36 canonical work pages

  1. [1]

    Schumacher, S., Hall, R., Waldman -Brown, A., & Sanneman, L. (2022). Technology Adoption of Collaborative Robots for Welding in Small and Medium-sized Enterprises: A Case Study Analysis. In Proceedings of the Conference on Production Systems and Logistics: CPSL 2022 (pp. 462- 471). Hannover: publish-Ing

    work page 2022

  2. [2]

    Y., Tju, H

    Zeng, Y., Zhang, D., Chien, S. Y., Tju, H. S., Wiesse, C., Cao, F., ... & Chen, I. M. (2024). Task sensing and adaptive control for mobile manipulator in indoor painting application. IEEE/ASME Transactions on Mechatronics, 29(4), 2956-2963

    work page 2024

  3. [3]

    (2022, December)

    Saccuti, A., Monica, R., & Aleotti, J. (2022, December). A Comparative Analysis of Collaborative Robots for Autonomous Mobile Depalletizing Tasks. In 2022 Sixth IEEE International Conference on Robotic Computing (IRC) (pp. 34-38). IEEE

    work page 2022

  4. [4]

    J., Kim, D

    Lee, S. J., Kim, D. H., & Jeong, J. (2021). ROBOPPRESSO: design and implementation of robot -barista services using COBOT and IoT. The Journal of the Institute of Internet, Broadcasting and Communication, 21(2), 177-186

    work page 2021

  5. [5]

    H., Mohabbat, A

    Yang, J., Lim, K. H., Mohabbat, A. B., Fokken, S. C., Johnson, D. E., Calva, J. J., ... & Bauer, B. A. (2024). Robotics in Massage: A Systematic Review. Health Services Research and Managerial Epidemiology , 11, 23333928241230948

    work page 2024

  6. [6]

    Cheong, A., Lau, M. W. S., Foo, E., Hedley, J., & Bo, J. W. (2016). Development of a robotic waiter system. IFAC -PapersOnLine, 49(21), 681-686

    work page 2016

  7. [7]

    C., Angulo, Y

    Moreno, M. C., Angulo, Y. D., & Suarez, O. J. (2022, November). Teleoperated robot prototype for the manipulation and transport of chemical substances in laboratory: a low -cost adaptation. In 2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA) (pp. 1-6). IEEE

    work page 2022

  8. [8]

    Qian, K., Song, A., Bao, J., & Zhang, H. (2012). Small teleoperated robot for nuclear radiation and chemical leak detection. International Journal of Advanced Robotic Systems, 9(3), 70

    work page 2012

  9. [9]

    (2018, May)

    Di Lillo, P., Di Vito, D., Simetti, E., Casalino, G., & Antonelli, G. (2018, May). Satellite-based tele-operation of an underwater vehicle-manipulator system. Preliminary experimental results. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 7504-7509). IEEE

    work page 2018

  10. [10]

    Feng, Y., Huang, W., & Chen, I. M. (2024, July). Optimizing Small-Scale Commercial Automation: Introducing WOS, a Low -Code Solution for Robotic Arms Integration. In 2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) (pp. 272-277). IEEE

    work page 2024

  11. [11]

    Macenski, S., Foote, T., Gerkey, B., Lalancette, C., & Woodall, W. (2022). Robot operating system 2: Design, architecture, and uses in the wild. Science robotics, 7(66), eabm6074

    work page 2022

  12. [12]

    Metta, G., Fitzpatrick, P., & Natale, L. (2006). YARP: yet another robot platform. International Journal of Advanced Robotic Systems, 3(1), 8

    work page 2006

  13. [13]

    Behere, S. (2010). A Generic Framework for Robot Motion Planning and Control

    work page 2010

  14. [14]

    Y., Gonzalez -Banos, H., Ng -Thow-Hing, V., & Davis, J

    Yang, A. Y., Gonzalez -Banos, H., Ng -Thow-Hing, V., & Davis, J. E. (2005, July). Robotalk: Controlling arms, bases and androids through a single motion interface. In ICAR'05. Proceedings., 12th International Conference on Advanced Robotics, 2005. (pp. 280-287). IEEE

    work page 2005

  15. [15]

    Coleman, D., Sucan, I., Chitta, S., & Correll, N. (2014). Reducing the barrier to entry of complex robotic software: a moveit! case study. arXiv preprint arXiv:1404.3785

    work page Pith review arXiv 2014

  16. [16]

    M., Vasudevan, S., Mohammed, W

    Fresnillo, P. M., Vasudevan, S., Mohammed, W. M., Lastra, J. L. M., & Garcia, J. A. P. (2023). Extending the motion planning framework—moveit with advanced manipulation functions for industrial applications. Robotics and Computer-Integrated Manufacturing, 83, 102559

    work page 2023

  17. [17]

    Palchaudhary, S., Narvekar, N., & Siddiqui, O. (2022). Path Planning and Path Optimization for Industrial Robots Using Moveit! and ROS Melodic (No. 2022-01-0348). SAE Technical Paper

    work page 2022

  18. [18]

    Barland, M. (2012). Motion Planning Framework for Industrial Manipulators using the Open Motion Planning Library (OMPL) (Master's thesis, Institutt for teknisk kybernetikk)

    work page 2012

  19. [19]

    T., Nguyen, T

    Vuong, T. T., Nguyen, T. P., & Nguyen, M. T. (2023). SMOOTH AND TIME OPTIMIZATION TRAJECTORY PLANNING FOR ROBOTS USING POLYNOMIAL INTERPOLATION. UTEHY Journal of Applied Science and Technology, 39, 8-14

    work page 2023

  20. [20]

    Xu, J., Ren, C., & Chang, X. (2023). Robot time -optimal trajectory planning based on quintic polynomial interpolation and improved Harris Hawks algorithm. Axioms, 12(3), 245

    work page 2023

  21. [21]

    Teng, S., Jasour, A., Vasudevan, R., & Ghaffari, M. (2023). Convex geometric motion planning on lie groups via moment relaxation. arXiv preprint arXiv:2305.13565

    work page arXiv 2023

  22. [22]

    Ata, A. A. (2007). Optimal trajectory planning of manipulators: a review. Journal of Engineering Science and technology, 2(1), 32-54

    work page 2007

  23. [23]

    Liu, H., Lai, X., & Wu, W. (2013). Time -optimal and jerk -continuous trajectory planning for robot manipulators with kinematic constraints. Robotics and Computer -Integrated Manufacturing , 29(2), 309-317

    work page 2013

  24. [24]

    (2015, November)

    Beeson, P., & Ames, B. (2015, November). TRAC -IK: An open -source library for improved solving of generic inverse kinematics. In 2015 IEEE- RAS 15th International Conference on Humanoid Robots (Humanoids) (pp. 928-935). IEEE

    work page 2015

  25. [25]

    Chen, Y., & Hu, H. (2013). Internet of intelligent things and robot as a service. Simulation Modelling Practice and Theory, 34, 159-171

    work page 2013

  26. [26]

    S., Pellier, D., Fiorino, H., & Pesty, S

    Liang, Y. S., Pellier, D., Fiorino, H., & Pesty, S. (2022). iRoPro: An interactive robot programming framework. International Journal of Social Robotics, 14(1), 177-191

    work page 2022

  27. [27]

    (2024, September)

    Ge, Y., Dai, Y., Shan, R., Li, K., Hu, Y., & Sun, X. (2024, September). Cocobo: Exploring Large Language Models as the Engine for End -User Robot Programming. In 2024 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) (pp. 89-95). IEEE

    work page 2024

  28. [28]

    Kehoe, B., Patil, S., Abbeel, P., & Goldberg, K. (2015). A survey of research on cloud robotics and automation. IEEE Transactions on automation science and engineering, 12(2), 398-409

    work page 2015

  29. [29]

    V., Lee, J., Jenkins, O

    Toris, R., Kammerl, J., Lu, D. V., Lee, J., Jenkins, O. C., Osentoski, S., ... & Chernova, S. (2015, September). Robot web tools: Efficient messaging for cloud robotics. In 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 4530-4537). IEEE

    work page 2015

  30. [30]

    Joshi, R., Didier, P., Holmberg, C., Jimenez, J., & Carey, T. (2022). The industrial internet of things connectivity framework. An Industry IoT Consortium, Foundational Document, 06-08

    work page 2022

  31. [31]

    (2015, May)

    Sredojev, B., Samardzija, D., & Posarac, D. (2015, May). WebRTC technology overview and signaling solution design and implementation. In 2015 38th international convention on information and communication technology, electronics and microelectronics (MIPRO) (pp. 1006 -1009). IEEE

    work page 2015

  32. [32]

    Zhang, X., Wang, D., Han, S., Li, W., Zhao, B., Wang, Z., ... & He, J. (2023). Affordance -driven next -best-view planning for robotic grasping. arXiv preprint arXiv:2309.09556

    work page arXiv 2023

  33. [33]

    Breyer, M., Furrer, F., Novkovic, T., Siegwart, R., & Nieto, J. (2018). Flexible robotic grasping with sim -to-real transfer based reinforcement learning. arXiv preprint arXiv:1803.04996

    work page arXiv 2018

  34. [34]

    Stellato, B., Banjac, G., Goulart, P., Bemporad, A., & Boyd, S. (2020). OSQP: An operator splitting solver for quadratic programs. Mathematical Programming Computation, 12(4), 637-672

    work page 2020

  35. [35]

    & Levine, S

    Luo, J., Xu, C., Liu, F., Tan, L., Lin, Z., Wu, J., ... & Levine, S. (2023). Fmb: a functional manipulation benchmark for generalizable robotic learning. The International Journal of Robotics Research, 02783649241276017

    work page 2023

  36. [36]

    Wang, X., Zhang, R., Kong, T., Li, L., & Shen, C. (2020). Solov2: Dynamic and fast instance segmentation. Advances in Neural information processing systems, 33, 17721-17732

    work page 2020