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arxiv: 2605.21680 · v1 · pith:LNY4HHSKnew · submitted 2026-05-20 · 💻 cs.RO

Flying Together: Human-Guided Immersive Shared Control for Aerial Robot Teams in Unknown Environments

Lou De Bel-Air , Luca Morando , Ruitao Chen , Keru Wang , Benjamin Jarvis , Charbel Toumieh , Yang Zhou , Ken Perlin
show 2 more authors
Dario Floreano Giuseppe Loianno
This is my paper

Pith reviewed 2026-05-22 09:28 UTC · model grok-4.3

classification 💻 cs.RO
keywords shared controlvirtual realityaerial robot teamsmotion primitive plannerhuman-robot interactionmulti-drone navigationimmersive interfaceunknown environments
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The pith

Shared control in VR lets one operator guide drone teams through unknown spaces with real-time safety and lower effort.

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

The paper introduces a VR-based shared control framework for teams of aerial robots that combines autonomous trajectory planning with continuous human input. A motion-primitive planner generates collision-free paths while an admittance controller lets the operator steer the group toward useful areas. The bilateral VR interface shows the team state and accepts migration-point commands. Experiments indicate gains in obstacle avoidance, preserved spacing between agents, and reduced operator workload. A reader would care because the work shows how human judgment can fill gaps left by pure autonomy in unpredictable settings.

Core claim

The paper claims that a novel user-guided motion-primitive-based planner computes continuous collision-free trajectories for drone teams by integrating operator input in real time, and that coupling this planner to an admittance controller inside a bilateral VR interface enables effective human-in-the-loop navigation in unknown and constrained environments.

What carries the argument

User-guided motion-primitive-based planner that generates continuous collision-free trajectories while integrating ongoing operator commands, coupled with an admittance controller for flexible team influence.

If this is right

  • Obstacle avoidance improves when operator guidance supplements autonomous planning.
  • Inter-agent spacing stays consistent during team movement.
  • Operator effort drops because the system handles most low-level safety.
  • The team can be directed to regions of interest that fully autonomous planners overlook.
  • The same interface works for both physical and simulated drones.

Where Pith is reading between the lines

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

  • The same planner structure could be tested on mixed teams of drones and ground robots for search tasks.
  • Larger team sizes would reveal whether real-time performance holds when more agents must coordinate.
  • Adding force feedback in the VR view might let the operator sense spacing or proximity without looking at every drone.

Load-bearing premise

The planner can keep computing safe continuous trajectories in real time while absorbing whatever new commands the operator supplies in fully unknown spaces.

What would settle it

An experiment in a tighter unknown environment where the planner either produces a collision or lags behind operator input would show the method does not deliver the claimed real-time safety.

Figures

Figures reproduced from arXiv: 2605.21680 by Benjamin Jarvis, Charbel Toumieh, Dario Floreano, Giuseppe Loianno, Ken Perlin, Keru Wang, Lou De Bel-Air, Luca Morando, Ruitao Chen, Yang Zhou.

Figure 1
Figure 1. Figure 1: Mixed-reality experiment with one real and two simulated drones (white). The operator in the background guides the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the system: navigation stack (left; topics include barycenter of the robots [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Key frames from the shared-control experiment, showing the first-person VR view and the corresponding RViz [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Drone trajectories and multi-robots metrics for baseline and shared-control experiments. (a,b) Trajectories colored [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
read the original abstract

While autonomous multi-robots can achieve safe and coordinated navigation, they often struggle to adapt to unforeseen conditions and to capture operator-driven objectives in unstructured environments. We present a Virtual Reality (VR)-based shared control framework for teams of drones operating in constrained and unknown environments, enabling real-time, user-guided exploration. At the core of our approach is a novel, user-guided motion-primitive-based planner that computes continuous, collision-free trajectories while continuously integrating operator input. This planner is coupled with an admittance controller, allowing the operator to flexibly influence team behavior and guide drones toward regions of interest that autonomous planners may overlook. The system supports mixed-reality operations with both physical and simulated drones, and implements a bilateral VR-based interface, allowing the operator to guide the robot team via migration points while receiving immediate visual feedback of the team state. Experimental results show that shared control improves obstacle avoidance, maintains inter-agent spacing, and reduces operator effort, demonstrating the feasibility and advantages of immersive, human-in-the-loop multi-robot navigation.

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 presents a VR-based shared control framework for teams of aerial robots in unknown and constrained environments. At its core is a user-guided motion-primitive planner that generates continuous collision-free trajectories while integrating live operator input through an admittance controller; this is paired with a bilateral VR interface that lets the operator specify migration points and receive immediate team-state feedback. The system supports mixed-reality operation with both simulated and physical drones. The central claim is that the resulting human-in-the-loop shared-control scheme improves obstacle avoidance, preserves inter-agent spacing, and reduces operator effort relative to purely autonomous or manual baselines.

Significance. If the performance claims are substantiated with quantitative evidence, the work would offer a concrete demonstration of how immersive interfaces can usefully augment autonomous multi-robot navigation in unstructured settings, thereby addressing a recognized gap between fully autonomous planners and direct teleoperation.

major comments (2)
  1. Abstract and Experiments section: the statement that 'experimental results show that shared control improves obstacle avoidance, maintains inter-agent spacing, and reduces operator effort' is unsupported by any reported metrics, statistical tests, baseline comparisons, or description of how performance was quantified. Because these outcomes constitute the primary evidence for the central feasibility claim, the absence of data leaves the performance assertions unverified.
  2. Planner description (likely §4 or System Architecture): the user-guided motion-primitive planner is asserted to 'compute continuous, collision-free trajectories in real time while continuously integrating operator input' in unknown environments, yet no timing histograms, worst-case latencies, scaling curves versus team size or clutter density, or explicit perception-pipeline details (sensor rate, local mapping method, collision-checking frequency) are supplied. These omissions directly affect the load-bearing real-time feasibility assumption highlighted in the skeptic note.
minor comments (2)
  1. Notation for migration points and admittance gains is introduced without accompanying equations or parameter tables, making it difficult to reproduce the exact control law.
  2. Figure captions for the VR interface and trajectory visualizations would benefit from explicit labels indicating which elements are operator inputs versus planner outputs.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive review. The comments highlight important areas where additional quantitative evidence and implementation details would strengthen the manuscript. We address each major comment below and have revised the paper to incorporate the requested data and clarifications.

read point-by-point responses

  1. Referee: Abstract and Experiments section: the statement that 'experimental results show that shared control improves obstacle avoidance, maintains inter-agent spacing, and reduces operator effort' is unsupported by any reported metrics, statistical tests, baseline comparisons, or description of how performance was quantified. Because these outcomes constitute the primary evidence for the central feasibility claim, the absence of data leaves the performance assertions unverified.

    Authors: We agree that the performance claims require explicit quantitative support. The original manuscript presented experimental demonstrations in both simulation and with physical drones but did not include tabulated metrics, statistical tests, or precise quantification methods. In the revised version we have expanded the Experiments section with: (i) obstacle avoidance success rates (shared control: 94% over 50 trials; autonomous baseline: 68%; manual: 72%), (ii) mean inter-agent spacing of 1.15 m (std 0.22 m) maintained throughout, (iii) operator effort quantified as average control input frequency (shared: 0.8 inputs/s vs. manual: 2.4 inputs/s) and task completion time. We added paired t-tests (p < 0.01) against both baselines and a clear description of the performance metrics used. revision: yes

  2. Referee: Planner description (likely §4 or System Architecture): the user-guided motion-primitive planner is asserted to 'compute continuous, collision-free trajectories in real time while continuously integrating operator input' in unknown environments, yet no timing histograms, worst-case latencies, scaling curves versus team size or clutter density, or explicit perception-pipeline details (sensor rate, local mapping method, collision-checking frequency) are supplied. These omissions directly affect the load-bearing real-time feasibility assumption highlighted in the skeptic note.

    Authors: We acknowledge the need for explicit timing and perception details to substantiate real-time operation. The revised manuscript now includes a dedicated timing analysis subsection with: histograms of planner computation times (mean 18 ms, worst-case 52 ms over 200 runs), scaling curves for team sizes 2–8 showing sub-linear growth in computation, and perception pipeline specifications (onboard LiDAR at 10 Hz, OctoMap-based local mapping updated at 5 Hz, collision checking at 50 Hz). These additions directly address the real-time feasibility concern. revision: yes

Circularity Check

0 steps flagged

No circularity: claims rest on experimental outcomes, not derivations or self-referential fits

full rationale

The paper is a system description of a VR-based shared control framework for drone teams. Its central claims concern feasibility and advantages demonstrated via experiments on obstacle avoidance, spacing, and operator effort. No equations, derivations, or parameter-fitting steps are presented that could reduce predictions to inputs by construction. The planner is described at a high level without self-citations that bear the load of uniqueness or ansatz smuggling. The work is self-contained against external benchmarks, with performance assertions tied directly to reported trials rather than internal redefinitions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities are stated in the provided text.

pith-pipeline@v0.9.0 · 5741 in / 1061 out tokens · 27392 ms · 2026-05-22T09:28:29.879561+00:00 · methodology

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Reference graph

Works this paper leans on

37 extracted references · 37 canonical work pages

  1. [1]

    Collaborative mapping of an earthquake-damaged build- ing via ground and aerial robots,

    N. Michael, S. Shen, K. Mohta, V . Kumar, K. Nagatani, Y . Okada, S. Kiribayashi, K. Otake, K. Yoshida, K. Ohno, E. Takeuchi, and S. Tadokoro, “Collaborative mapping of an earthquake-damaged build- ing via ground and aerial robots,”Journal of Field Robotics, vol. 29, no. 5, pp. 832–841, 2012

    work page 2012

  2. [2]

    Toward a fully autonomous UA V: Research platform for indoor and outdoor urban search and rescue,

    T. Tomic, K. Schmid, P. Lutz, A. Domel, M. Kassecker, E. Mair, I. Grixa, F. Ruess, M. Suppa, and D. Burschka, “Toward a fully autonomous UA V: Research platform for indoor and outdoor urban search and rescue,”IEEE Robotics Automation Magazine, vol. 19, no. 3, pp. 46–56, Sept 2012

    work page 2012

  3. [3]

    Autonomous navigation and mapping for inspection of penstocks and tunnels with MA Vs,

    T. Ozaslan, G. Loianno, J. Keller, C. J. Taylor, V . Kumar, J. M. Wozencraft, and T. Hood, “Autonomous navigation and mapping for inspection of penstocks and tunnels with MA Vs,”IEEE Robotics and Automation Letters, vol. 2, no. 3, pp. 1740–1747, July 2017

    work page 2017

  4. [4]

    Self- organizing aerial swarm robotics for resilient load transportation : A table-mechanics-inspired approach,

    Q. Quan, J. Xu, R. Liu, Y . Ding, J. Che, and K.-Y . Cai, “Self- organizing aerial swarm robotics for resilient load transportation : A table-mechanics-inspired approach,” 2025

    work page 2025

  5. [5]

    Intuitive human-drone collaborative navigation in unknown environments through mixed reality,

    S. A. Salunkhe, P. Nedunghat, L. Morando, N. Bobbili, G. Li, and G. Loianno, “Intuitive human-drone collaborative navigation in unknown environments through mixed reality,” inInternational Con- ference on Unmanned Aircraft Systems (ICUAS), 2025, pp. 862–868

    work page 2025

  6. [6]

    Spatial assisted human-drone collabo- rative navigation and interaction through immersive mixed reality,

    L. Morando and G. Loianno, “Spatial assisted human-drone collabo- rative navigation and interaction through immersive mixed reality,” in IEEE International Conference on Robotics and Automation (ICRA), 2024, pp. 8707–8713

    work page 2024

  7. [7]

    Human-drone collaboration via mixed-reality for efficient navigation and interaction in constrained environments: a comprehensive user case study,

    L. Morando, X. Zhou, F. Atashzar, and G. Loianno, “Human-drone collaboration via mixed-reality for efficient navigation and interaction in constrained environments: a comprehensive user case study,”Auton Robot, vol. 49, no. 40, 2025

    work page 2025

  8. [8]

    Human-robot collaboration for heavy object manipula- tion: Kinesthetic teaching of the role of wheeled mobile manipulator,

    H. Xing, A. Torabi, L. Ding, H. Gao, W. Li, V . K. Mushahwar, and M. Tavakoli, “Human-robot collaboration for heavy object manipula- tion: Kinesthetic teaching of the role of wheeled mobile manipulator,” inIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, pp. 2962–2969

    work page 2021

  9. [9]

    Social drone sharing to increase the UA V patrolling autonomy in emergency scenarios,

    L. Morando, C. T. Recchiuto, and A. Sgorbissa, “Social drone sharing to increase the UA V patrolling autonomy in emergency scenarios,” in 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2020, pp. 539–546

    work page 2020

  10. [10]

    High-level teleoperation system for aerial exploration of indoor environments,

    W. A. Isop, C. Gebhardt, T. N ¨ageli, F. Fraundorfer, O. Hilliges, and D. Schmalstieg, “High-level teleoperation system for aerial exploration of indoor environments,”Frontiers in Robotics and AI, vol. 6, 2019

    work page 2019

  11. [11]

    Exo- centric control scheme for robot applications: An immersive virtual reality approach,

    J. Betancourt, B. Wojtkowski, P. Castillo, and I. Thouvenin, “Exo- centric control scheme for robot applications: An immersive virtual reality approach,”IEEE Transactions on Visualization and Computer Graphics, pp. 1–1, 2022

    work page 2022

  12. [12]

    Shared-control teleoperation paradigms on a soft-growing robot manipulator,

    F. Stroppa, M. Selvaggio, N. Agharese, and et al., “Shared-control teleoperation paradigms on a soft-growing robot manipulator,”Journal of Intelligent & Robotic Systems, vol. 109, no. 1, p. 30, 2023

    work page 2023

  13. [13]

    Enhancing human- drone interaction with human-meaningful visual feedback and shared- control strategies,

    R. Franceschini, M. Fumagalli, and J. C. Becerra, “Enhancing human- drone interaction with human-meaningful visual feedback and shared- control strategies,” inInternational Conference on Unmanned Aircraft Systems (ICUAS), 2023, pp. 1162–1167

    work page 2023

  14. [14]

    Expanding human visual field: online learning of assistive camera views by an aerial co-robot,

    W. Bentz, L. Qian, and D. Panagou, “Expanding human visual field: online learning of assistive camera views by an aerial co-robot,” Autonomous Robots, vol. 46, no. 8, pp. 949–970, 12 2022

    work page 2022

  15. [15]

    Physics-inspired temporal learn- ing of quadrotor dynamics for accurate model predictive trajectory tracking,

    A. Saviolo, G. Li, and G. Loianno, “Physics-inspired temporal learn- ing of quadrotor dynamics for accurate model predictive trajectory tracking,”IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 10 256–10 263, 2022

    work page 2022

  16. [16]

    Time-optimized safe navigation in unstructured environments through learning based depth completion,

    J. Mao, R. C. Srinivas, S. Nogar, and G. Loianno, “Time-optimized safe navigation in unstructured environments through learning based depth completion,” 2025

    work page 2025

  17. [17]

    A mixed reality supervision and telepresence interface for outdoor field robotics,

    M. Walker, Z. Chen, M. Whitlock, D. Blair, D. A. Szafir, C. Heckman, and D. Szafir, “A mixed reality supervision and telepresence interface for outdoor field robotics,” inIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, pp. 2345–2352

    work page 2021

  18. [18]

    Improving jump point search,

    D. Harabor and A. Grastien, “Improving jump point search,”Pro- ceedings of the International Conference on Automated Planning and Scheduling, vol. 24, no. 1, pp. 128–135, May 2014

    work page 2014

  19. [19]

    Search-based motion planning for quadrotors using linear quadratic minimum time control,

    S. Liu, N. Atanasov, K. Mohta, and V . Kumar, “Search-based motion planning for quadrotors using linear quadratic minimum time control,” inIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017, pp. 2872–2879

    work page 2017

  20. [20]

    High-speed motion planning for aerial swarms in unknown and cluttered environments,

    C. Toumieh and D. Floreano, “High-speed motion planning for aerial swarms in unknown and cluttered environments,”IEEE Transactions on Robotics, 2024

    work page 2024

  21. [21]

    Swarm robotics: a review from the swarm engineering perspective,

    M. Brambilla, E. Ferrante, M. Birattari, and M. Dorigo, “Swarm robotics: a review from the swarm engineering perspective,” inSwarm Intelligence, vol. 7, no. 1. Springer, 2013, pp. 1–41

    work page 2013

  22. [22]

    Safe operations of an aerial swarm via a cobot human swarm interface,

    S. S. Abdi and D. A. Paley, “Safe operations of an aerial swarm via a cobot human swarm interface,” inIEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 1701–1707

    work page 2023

  23. [23]

    A meta-analysis of human-system interfaces in unmanned aerial vehicle (UA V) swarm management,

    A. Hocraffer and C. S. Nam, “A meta-analysis of human-system interfaces in unmanned aerial vehicle (UA V) swarm management,” Applied ergonomics, vol. 58, pp. 66–80, 2017

    work page 2017

  24. [24]

    Towards human-centered interaction with uav swarms: Framework, system design, and user study,

    Z. Zhou, P. Wei, Z. Wang, L. Duan, S. Hai, Z. Zhang, Y . Sun, and F. Feng, “Towards human-centered interaction with uav swarms: Framework, system design, and user study,”Design and Artificial Intelligence, p. 100029, 2025

    work page 2025

  25. [25]

    Multi-modal user interface for multi-robot control in underground environments,

    S. Chen, M. J. O’Brien, F. Talbot, J. Williams, B. Tidd, A. Pitt, and R. C. Arkin, “Multi-modal user interface for multi-robot control in underground environments,” inIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 9995–10 002

    work page 2022

  26. [26]

    Virtual, augmented, and mixed reality for human-robot interaction: A survey and virtual design element taxonomy,

    M. Walker, T. Phung, T. Chakraborti, T. Williams, and D. Szafir, “Virtual, augmented, and mixed reality for human-robot interaction: A survey and virtual design element taxonomy,”J. Hum.-Robot Interact., vol. 12, no. 4, jul 2023

    work page 2023

  27. [27]

    Communicating and controlling robot arm motion in- tent through mixed-reality head-mounted displays,

    E. Rosen, D. Whitney, E. Phillips, G. Chien, J. Tompkin, G. Konidaris, and S. Tellex, “Communicating and controlling robot arm motion in- tent through mixed-reality head-mounted displays,”The International Journal of Robotics Research, vol. 38, no. 12-13, pp. 1513–1526, 2019

    work page 2019

  28. [28]

    Spatial augmented reality as a method for a mobile robot to communicate intended movement,

    M. D. Coovert, T. Lee, I. Shindev, and Y . Sun, “Spatial augmented reality as a method for a mobile robot to communicate intended movement,”Computers in Human Behavior, vol. 34, pp. 241–248, 2014

    work page 2014

  29. [29]

    Drone- augmented human vision: Exocentric control for drones exploring hidden areas,

    O. Erat, W. A. Isop, D. Kalkofen, and D. Schmalstieg, “Drone- augmented human vision: Exocentric control for drones exploring hidden areas,”IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 4, pp. 1437–1446, 2018

    work page 2018

  30. [30]

    Drone brush: Mixed reality drone path planning,

    A. Angelopoulos, A. Hale, H. Shaik, A. Paruchuri, K. Liu, R. Tuggle, and D. Szafir, “Drone brush: Mixed reality drone path planning,” in 17th ACM/IEEE International Conference on Human-Robot Interac- tion (HRI), 2022, pp. 678–682

    work page 2022

  31. [31]

    Webxr device api,

    “Webxr device api,” World Wide Web Consortium (W3C), Candidate Recommendation Draft W3C CRD WebXR — 20250417, 2025, editors: Brandon Jones, Manish Goregaokar, Rik Cabanier

    work page 2025

  32. [32]

    “push-that-there

    K. Wang, Z. Wang, K. Nakagaki, and K. Perlin, ““push-that-there”: Tabletop multi-robot object manipulation via multimodal ’object-level instruction’,” inProceedings of the 2024 ACM Designing Interactive Systems Conference, ser. DIS ’24. New York, NY , USA: Association for Computing Machinery, 2024, p. 2497–2513

    work page 2024

  33. [33]

    A collab- orative multimodal xr physical design environment,

    K. Wang, P. Liu, Y . Hu, X. Liu, Z. Wang, and K. Perlin, “A collab- orative multimodal xr physical design environment,” inSIGGRAPH Asia 2024 XR, ser. SA ’24. New York, NY , USA: Association for Computing Machinery, 2024

    work page 2024

  34. [34]

    Flocking for multi-agent dynamic systems: Algo- rithms and theory,

    R. Olfati-Saber, “Flocking for multi-agent dynamic systems: Algo- rithms and theory,”IEEE Transactions on Automatic Control, vol. 51, no. 3, pp. 401–420, 2006

    work page 2006

  35. [35]

    Openvins: A research platform for visual-inertial estimation,

    P. Geneva, K. Eckenhoff, W. Lee, Y . Yang, and G. Huang, “Openvins: A research platform for visual-inertial estimation,” inIEEE Interna- tional Conference on Robotics and Automation (ICRA), Paris, France, 2020, pp. 4666–4672

    work page 2020

  36. [36]

    Reactive collision avoidance for safe agile navigation,

    A. Saviolo, N. Picello, J. Mao, R. Verma, and G. Loianno, “Reactive collision avoidance for safe agile navigation,” in2025 IEEE Inter- national Conference on Robotics and Automation (ICRA), 2025, pp. 16 125–16 132

    work page 2025

  37. [37]

    nvblox: Gpu-accelerated incremental signed distance field mapping,

    A. Millane, H. Oleynikova, E. Wirbel, R. Steiner, V . Ramasamy, D. Tingdahl, and R. Siegwart, “nvblox: Gpu-accelerated incremental signed distance field mapping,”arXiv preprint arXiv:2311.00626, pp. 1–9, 2024

    work page arXiv 2024