Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains

Ewerton de Oliveira; Lucas Nogueira Nobrega; Martin Saska; Tiago Nascimento

REVIEW 4 major objections 4 minor 24 references

Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains

T0 review · 4 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This paper aims to show that a federated-learning-trained LSTM classifier can give a UAV proximal gesture control, and that a second UAV prevents occlusion by taking over detection when the first loses sight of the operator.

desk verdict The abstract's >96% real-robot accuracy claim is contradicted by the paper's own 68.67% real-time result; the real contribution is a working FL-LSTM multi-UAV handover demo and dataset, but the paper overstates it. read the letter →

arxiv 2412.02863 v2 pith:POT5VFEA submitted 2024-12-03 cs.RO cs.AIcs.LG

Lucas Nogueira Nobrega , Ewerton de Oliveira , Martin Saska , Tiago Nascimento This is my paper · ORCID

classification cs.ROcs.AIcs.LG

keywords federatedlearninghuman-robotinteractionUAVcontrolactionrecognitionLSTMgestureocclusionmulti-robotsystems

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The authors aim to establish that a lightweight LSTM action classifier, trained distributively through federated learning on multiple drones, can translate human gestures into drone commands, and that a multi-drone setup solves the occlusion problem by letting a secondary drone take over whenever the main drone's view is blocked. They claim this is the first use of federated learning in human-robot interaction for proximal UAV control. In support, they present offline validation accuracy of 96–97% on their own three-view dataset, and real-robot trials in which the view handover successfully executed a command sequence. The same trials, however, report a real-time accuracy of 0.6867 with precision 0.93 and F1 0.83, which the authors attribute to distance, sensor quality, and lighting conditions.

What carries the argument

The load-bearing mechanism is a two-layer LSTM followed by three dense layers (with dropout) that operates on a sliding window of 60 frames of 33 MediaPipe body keypoints (x, y, z coordinates), and is trained across clients by federated averaging. The occlusion countermeasure is a priority check: the secondary drone's commands are ignored whenever the primary drone detects the operator, so the second viewpoint acts as a fallback transmitter. This combination—the streaming keypoint window and the priority-checked viewpoint handover—carries the argument.

What would settle it

A controlled reproduction that runs the same trained model on the same drone platforms while varying operator distance, lighting, and camera quality in the ranges described in Section VI-B, and measures end-to-end command accuracy, would settle whether the offline accuracy transfers; if accuracy stays near 0.69 rather than above 0.96, the paper's headline accuracy claim is not supported.

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Extended reading notes

Core claim

The central claim is that an LSTM with two recurrent layers and three dense layers, trained with federated averaging across drone-mounted clients, classifies 13 gesture/action classes from MediaPipe body keypoints well enough to command a UAV, and that a second drone can take over command when the primary drone loses sight of the operator. The paper reports training and validation accuracy of 96–97% on its own dataset (Table III) and a real-time system accuracy of 0.6867, with precision 0.93 and F1 0.83 in the real-robot trials. The authors state that this is the first work applying federated learning to proximal control of UAVs in human-robot interaction.

Load-bearing premise

The load-bearing premise is that the high offline validation accuracy of 96–97% transfers to real-robot closed-loop operation, despite the paper reporting a real-time accuracy of 0.6867 in Section VI-B.

Editorial extensions

If this is right

Gesture-based proximal control of UAVs can be trained without centralizing operator video data, since only model weights are shared across drones.
Multi-robot teams can maintain continuous command of a drone by viewpoint handover, because the secondary robot's detection covers the primary robot's blind spots.
The same federated architecture extends to more than two drones or cameras, as the authors note, so larger swarms could share a single action model.
A streaming action classifier with a 10.4-second command window can be deployed onboard with modest compute, as demonstrated on the X500 drone.
The work offers a comparison between a literature dataset and a new three-view dataset, providing a baseline for future gesture-control UAV research.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If federated training converges in only three rounds with two clients, the value of FL here may be less about privacy or communication savings than about pooling training signal from multiple viewpoints; a direct comparison against a centrally trained model on the same data would separate these effects.
The gap between 96–97% offline validation accuracy and 0.6867 real-time accuracy suggests that robustness to lighting, distance, and sensor noise is the actual bottleneck; a controlled test that varies these conditions systematically could guide targeted data augmentation.
The 60-frame sliding window and the 10-second wait between commands restrict the system to discrete, slow command sequences; extending to continuous velocity commands would require a different action set and temporal decoding, a natural next step the paper does not address.
The novelty claim of first use of federated learning in proximal UAV control rests on the completeness of the cited literature; none of the gesture-control works referenced use FL, which supports the claim but does not prove it.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

Read this one if you care about how federated learning gets bolted onto HRI. The punchline: the advertised headline number is wrong. The abstract says real-robot accuracy greater than 96%; Section VI-B reports real-time accuracy 0.6867. That is a direct contradiction. Table III's 96–97% is model validation on their dataset2, not closed-loop robot performance.

What is actually here: a plausible integration of known components—Mediapipe pose, a two-layer LSTM, federated averaging via Flower, and a two-drone setup where the X500 and DJI cover each other's occlusion. The real-robot experiment shows the handover working: when the X500 loses the operator, the DJI picks up the command. They also release a dataset and code, which is reproducible evidence and worth something. The writing is clear, and the pipeline description is honest about the wait times and filtering.

The soft spots are real. The accuracy contradiction is load-bearing. The authors explain the drop by distance, sensor quality, and lighting, but that does not repair the mismatch with the abstract. There is no non-FL baseline, so we do not learn whether federated training actually helps or hurts. No error bars, no repeated trials beyond one sequence. And the 'absence of overfitting' argument—distinguishing a wrong prediction from no detection—does not test overfitting at all.

As is, rejection is the right call because the central claim fails. But the system works, the dataset is useful, and the occlusion handover is a nice demonstration. With a corrected abstract, honest reporting of validation versus real-robot accuracy, and a baseline comparison, this could become a decent short paper. The novelty is integration, not components, and the authors say as much.

For peer review: I would send it out. A referee can catch the contradiction and force the fix, and the dataset and repo make the work checkable. Expect major revision.

Referee Report

4 major / 4 minor

Summary. The paper proposes a federated-learning (FedAvg) pipeline for LSTM-based action recognition from Mediapipe keypoints, used for proximal gesture control of a UAV, with a second UAV maintaining the human operator in view to avoid occlusions. The authors create a second gesture dataset, validate their model on two datasets, and report a real-robot demonstration with three clients and two drones. The abstract claims that experiments with real robots achieved accuracy greater than 96%, and the paper claims to be the first work using federated learning in HRI for proximal control of UAVs.

Significance. If the claims were supported, the paper would offer a practical demonstration of onboard federated LSTM action recognition for multi-UAV proximal control, with a public dataset and a real-robot occlusion-handover experiment. These are genuine strengths: the authors deployed two different UAV platforms, used standard FL tooling (Flower), report a confusion matrix, and provide a video link. However, because the headline real-robot accuracy is directly contradicted by the reported 68.67% real-time accuracy and because the FL component is not compared with any non-FL or centralized baseline, the significance of the contribution as stated is not established.

major comments (4)

[Abstract and Section VI-B] The abstract's claim that 'experiments with real robots achieving an accuracy greater than 96%' is directly contradicted by Section VI-B, which reports that 'in the real-time experiments, the Accuracy of our system was 0.6867.' Table III's 0.96/0.97 figures are model validation accuracies on dataset2, not closed-loop real-robot results. Since the paper's own real-robot experiment is the only direct evidence for the headline claim, the abstract-level result is unsupported and must be corrected.
[Sections V and VI-A] The federated-learning contribution is not evaluated against any relevant baseline. Section VI-A states that each client used the same dataset, rather than the non-IID partitions that motivate FL in Section V, and the paper reports no comparison with centralized training or with local-only training. Consequently, the statements that FL enables distributed training and accelerates convergence are not supported by the experiments.
[Section VI-B] The 'absence of overfitting' argument is not a valid test: distinguishing a wrong action prediction from a missed detection addresses error modes, not the generalization gap between training and deployment. The paper does not report a held-out real-robot protocol or per-trial variability, so the drop from 96-97% validation accuracy to 68.67% real-time accuracy remains unexplained and is attributed only to environmental factors.
[Section IV-2 and Table I] The created dataset is described as having 33 unique actions and 6 unique static gestures after multiplying 11 actions and 2 gestures by three viewpoints, yet the architecture in Table I has a 13-unit softmax output and Section III-B describes commands for 13 classes. If viewpoint-specific labels are used, the output layer cannot represent them; if they are mapped back to 13 classes, the '33 unique actions' statement is misleading. The label mapping needs clarification.

minor comments (4)

[Fig. 12] The captions alternate between 'Hover' and 'Have command' for what appears to be the same action; please standardize the action names.
[References] Reference [19] is cited for OpenPose but the arXiv identifier 2007.14390 actually belongs to the Flower framework, which is also cited as [24]; the OpenPose citation should be corrected.
[Section III-B] The phrase 'In resume' should be 'In summary'.
[Equation (1)] The symbol R is used both for the rotation matrix and for the set of unit vectors in equation (3); please use distinct notation to avoid ambiguity.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the paper is an empirical ML/robotics study, and its abstract accuracy claim, while internally inconsistent with the reported real-time accuracy, is not a definitional reduction.

full rationale

The paper contains no derivation chain whose output is equivalent to its input by construction. The proposed system is an LSTM action classifier trained with federated averaging; performance is measured empirically on two datasets (Perera et al. and the authors' own dataset) and in real-robot experiments. The dataset augmentation equation (1) defines a rotation operation on frames; it does not encode the classification outcome. Tables II and III report training/validation loss and accuracy; these are standard empirical metrics, not fitted parameters renamed as predictions. The real-robot accuracy of 0.6867 is reported separately in Section VI-B, and the abstract's '>96%' figure appears to be drawn from the dataset-2 validation accuracy of Table III rather than from the closed-loop real-time result. This is a serious reporting inconsistency and an evidentiary weakness, but it is not circularity because the validation metric is not defined in terms of the real-robot claim. The FL implementation follows the standard FedAvg algorithm from McMahan et al. [7], and the MRS system [23] is used as a component; these self-citations are not load-bearing in the sense of justifying a prediction by the authors' own prior unverified result. The 'first work using FL in HRI for proximal control' claim is a novelty statement, not a mathematical derivation. No step reduces to its own input, so the circularity score is 0.

Assumptions & free parameters 7 free parameters · 4 assumptions · 0 invented entities

The central claims rest on hand-picked hyperparameters (epochs, early stopping, FedAvg rounds, window sizes, augmentation range), on domain assumptions about camera consistency and DJI hover coverage, and on the unverified transfer of validation accuracy to real-time closed-loop operation. No new physical or algorithmic entities are introduced.

free parameters (7)

FedAvg rounds = 5
Selected empirically as sufficient because 'no significant improvement would have been achieved' with more rounds; no convergence analysis.
Training epochs / early stopping patience = 750 / 300
Found empirically; no sensitivity study.
Action recognition window = 60 frames
Fixed window for each classification decision; not varied.
Moving average window = 3 actions
Chosen to suppress transition errors; not ablated.
Command wait/debounce period = 10 seconds
Ten-second wait between commands; chosen by design, not optimized.
Data augmentation rotation range = -15 to +15 degrees
Arbitrarily chosen augmentation on keypoints; no ablation.
Network architecture sizes = LSTM 30/64, dense 64/32, output 13
Hand-designed; no hyperparameter search.

assumptions (4)

domain assumption FedAvg with only five rounds and two or three clients converges to a useable global model
Algorithm 1 and Section V assume convergence without convergence analysis or comparison to more rounds.
domain assumption The DJI UAV hovering at a fixed position keeps the operator in its field of view whenever the X500 loses sight
Explicit in Section III: 'for the sake of simplicity, we assume the DJI is only hovering in order to keep the operator always within the field of view of at least one of the UAVs.'
domain assumption Mediapipe skeleton keypoints from both UAV viewpoints are sufficiently consistent for a single shared LSTM classifier
Section III-A assumes keypoint coordinates from two different cameras can be fed to the same model without camera calibration or coordinate normalization.
ad hoc to paper Validation accuracy on the created dataset transfers to real-robot operation
Section VI-B reports a drop from 96-97% validation to 68.67% real-time accuracy; this assumption is load-bearing for the abstract's 96% claim.

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Cite this review

Pith. "Pith review of Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains." pith.science (2026). https://pith.science/paper/POT5VFEA

@misc{pith2026241202863,
  author       = {Pith},
  title        = {Pith review of: Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/POT5VFEA}},
  note         = {Machine review of arXiv:2412.02863}
}

read the original abstract

The human-robot interaction (HRI) is a growing area of research. In HRI, complex command (action) classification is still an open problem that usually prevents the real applicability of such a technique. The literature presents some works that use neural networks to detect these actions. However, occlusion is still a major issue in HRI, especially when using uncrewed aerial vehicles (UAVs), since, during the robot's movement, the human operator is often out of the robot's field of view. Furthermore, in multi-robot scenarios, distributed training is also an open problem. In this sense, this work proposes an action recognition and control approach based on Long Short-Term Memory (LSTM) Deep Neural Networks with two layers in association with three densely connected layers and Federated Learning (FL) embedded in multiple drones. The FL enabled our approach to be trained in a distributed fashion, i.e., access to data without the need for cloud or other repositories, which facilitates the multi-robot system's learning. Furthermore, our multi-robot approach results also prevented occlusion situations, with experiments with real robots achieving an accuracy greater than 96%.

Figures

Figures reproduced from arXiv: 2412.02863 by the authors.

**Figure 2.** Proposed Pipeline. The DJI UAV (red blocks) sends images to [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Operator Detection block. machine-learning pipelines, and which has a human joint detector. The output of Mediapipe is used to calculate the 33 points within the X, Y, and Z coordinates in the world frame. The frame key points are then sent to the action recognition algorithm. B. Action Recognition Our action recognition approach works on a streaming basis reconstructed from each processed frame that comes from the … view at source ↗

Figures from the paper (7 more)

**Figure 5.** Figure 5: A frame for each of the eleven actions and two gestures (static) [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]

**Figure 7.** Figure 7: Training results of two UAVs. Algorithm 1 Federated Average and the LSTM implementation. The K clients are indexed by k; B is the local minibatch size, E is the number of local epochs, and η is the learning rate procedure SERVER EXECUTES(a, b) initialize w0 for each r…

**Figure 8.** Figure 8: Real-time acquisition. Client A detects and sends the correct command [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]

**Figure 9.** Figure 9: Real-time acquisition: Client B detects and sends the wrong command [PITH_FULL_IMAGE:figures/full_fig_p006_9.png]

**Figure 10.** Figure 10: Simulated UAVs. TABLE II SUMMARY OF METRICS - DATASET 1 - CLIENT A AND B Client Loss Accuracy Time elapsed (seconds) A 2.29 0.99 235 B 3.08 0.75 233 VI. RESULTS Our experiments and simulations relied on Flower [24], a wildly popular framework for FL and from which we …

**Figure 12.** Figure 12: Human-UAV interaction experiment [PITH_FULL_IMAGE:figures/full_fig_p007_12.png]

**Figure 13.** Figure 13: Resulting confusion matrix from the experiments. [PITH_FULL_IMAGE:figures/full_fig_p007_13.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

Robots in the loop: Telepresence robots in everyday life,

K. M. Tsui, S. V on Rump, H. Ishiguro, L. Takayama, and P. Vicars, “Robots in the loop: Telepresence robots in everyday life,” inACM/IEEE HRI, Mar. 2012, pp. 317–318, iSSN: 2167-2148

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A. Chaudhary, T. Nascimento, and M. Saska, “Controlling a swarm of unmanned aerial vehicles using full-body k-nearest neighbor based action classifier.” IEEE, 6 2022, pp. 544–551

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