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REVIEW 3 major objections 6 minor 47 references

Neuromorphic Attitude Estimation and Control

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A spiking neural network trained by imitation can take raw inertial measurements from a real quadrotor and output motor commands directly, tracking attitude almost as well as the standard PID stack.

desk verdict A real, reproducible demonstration of end-to-end spiking attitude control on a quadrotor, but the estimation claim is under-validated and the headline RMSE comparison scores each controller on its own internal estimate. read the letter →

arxiv 2411.13945 v2 pith:HGRABPYK submitted 2024-11-21 cs.RO cs.LGcs.NE

Stein Stroobants , Christophe de Wagter , Guido C.H.E. De Croon This is my paper · ORCID
classification cs.ROcs.LGcs.NE
keywords spikingneuralnetworksneuromorphiccontrolattitudeestimationimitationlearningquadrotorend-to-endleaky-integrate-and-fireCrazyflie
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

This paper sets out to show that a spiking neural network can replace the entire low-level attitude loop of a real quadrotor: raw gyroscope and accelerometer readings go in, motor torque commands come out, with no classical estimator or PID controller in between. The authors train two spiking sub-networks separately by imitation learning—one that estimates attitude from sensor data and one that turns attitude into motor commands—and then merge them into a single network. Deployed on a 35-gram Crazyflie with an added microcontroller, the merged network runs at 500 hertz and tracks attitude commands with an average error of 3.03 degrees, compared with 2.67 degrees for the conventional flight stack. What matters for future autonomous drones is that the full perception-to-control pipeline is expressed in spiking neurons, a necessary step toward running all of a drone's autonomy on one low-power neuromorphic chip.

What carries the argument

The central object is a merged spiking neural network built from current-based leaky-integrate-and-fire (CUBA-LIF) neurons—neurons that keep a synaptic current and a membrane potential and emit a binary spike when the potential crosses a threshold. The architecture has two parts: a two-layer recurrent estimation sub-network that converts gyroscope and accelerometer readings into an internal attitude estimate, and a one-layer recurrent control sub-network that maps that estimate plus attitude setpoints into torque commands. Because both sub-networks connect to the outside world through linear weight matrices, the output weights of the estimator and the input weights of the controller are multiplied during merging, yielding one network with no classical estimator or PID in the loop. The mechanism that gives the network integral action is a small group of ten control-layer neurons whose leak and threshold parameters are frozen at 1, turning them into lossless integrators; the mechanism that removes the SNN's phase lag is training the control sub-network on targets shifted roughly six timesteps into the future. Together these mechanisms carry the argument that a spiking network can absorb the temporal operations—integration, differentiation, prediction—that a conventional flight stack performs with explicit filters and controller terms.

What would settle it

Run the deployed network through sustained large-angle attitude changes and rapid yaw reversals that are underrepresented in the training set while recording true orientation with an external motion-capture system; if the network's internal attitude readout deviates substantially from the motion-capture orientation even when the motor commands still appear reasonable, then the system is not performing generalizable attitude estimation.

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Extended reading notes

Core claim

On its own terms, the paper's central claim is that a fully spiking network can perform attitude estimation and control end-to-end on a real quadrotor. The network's inputs are the six raw IMU signals; its outputs are torque commands fed straight into the motor mixer. The authors train an estimation sub-network and a control sub-network separately, then merge them by multiplying their linear input and output weight matrices, so that deployment uses a single network with roughly 15 percent spiking activity. To make the approach work in flight, they add three ingredients to the imitation-learning procedure: ten neurons in the control layer have their leak and threshold parameters fixed to act as perfect integrators, absorbing gyroscope bias the way a PID integral term would; the training targets are shifted about six timesteps into the future so the network learns to predict the expert's next control action, compensating for the SNN's intrinsic delay; and the dataset is augmented with excitation and random disturbances so the network sees states beyond the stable hover regime. With these modifications, the SNN tracks commanded roll angles with 3.03 degrees RMSE (average standard deviation 0.77 degrees), while the PID/complementary-filter stack achieves 2.67 degrees RMSE. The authors read this as evidence that neuromorphic end-to-end control is feasible on current hardware.

Load-bearing premise

The central claim assumes that the estimation sub-network, which is merged with the control sub-network and never validated against external ground truth on the real drone, actually computes a generalizable attitude estimate rather than a mapping that only works for the maneuvers and disturbances seen during training.

Editorial extensions

If this is right

  • A single SNN can take over the low-level attitude loop on a real quadrotor, with tracking error within about 0.4 degrees of a conventional complementary-filter-plus-PID stack.
  • The train-then-merge recipe lets estimation and control be learned separately, which avoids the local minimum in which the estimator ignores sensor data and simply copies the command signal.
  • Training on time-shifted targets removes most of the SNN-induced delay and reduces oscillations, at the cost of roughly 15 milliseconds of additional rise time.
  • Fixed-parameter integrator neurons provide bias-rejecting integral action without the unstable recurrent gains that free training tends to produce.
  • Because inference is dominated by additions and spiking activity is sparse, the network would be substantially cheaper to run on native neuromorphic hardware than on the conventional microcontroller used in the experiments.

Reading between the lines

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

  • If the estimation sub-network indeed generalizes, the same modular estimation/control merge could be applied to other low-level loops—for example, optical-flow velocity estimation or wheel-encoder odometry—where a classical observer and a controller are both present.
  • The power comparison in the paper is based on operation counts rather than measured hardware, so we infer that the real payoff, and the most direct test of the approach, will come when the identical network runs on an actual neuromorphic chip instead of a general-purpose microcontroller.
  • The authors never compare the internal attitude estimate to external ground truth, so we regard a hidden-layer readout that matches a motion-capture orientation across aggressive maneuvers as the key confirmation that the estimation claim is real.
  • The time-shift trick suggests a general rule for spiking controllers: because leaky-integrate-and-fire dynamics intrinsically smooth and delay signals, training a network to predict the future reference output is a way to recover responsiveness without adding explicit latency compensation.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper presents a spiking neural network (SNN) for attitude estimation and control of a quadrotor. The network is trained by imitation learning from a reference complementary-filter-plus-PID stack, split into an estimation sub-network and a control sub-network that are merged after training, and deployed on a Crazyflie with a Teensy 4.0 microcontroller at 500 Hz. The authors report stable position tracking and attitude step responses on real flights, with a headline RMSE of 3.03 degrees versus 2.67 degrees for the regular PID stack. They also propose time-shifting training targets, augmenting the training data with disturbances and SNN-in-the-loop flight, and fixing a small subgroup of integrator neurons to stabilize training. The paper concludes with an operations-based energy analysis.

Significance. If the central claims are fully validated, this is a significant step: it would be the first demonstration of a spiking network mapping raw IMU data directly to motor commands for attitude control of a real quadrotor, with stable flight, and with code and training data publicly released. Strong points include the real-robot experiments (ten runs per condition), the modular estimation/control architecture, the practical integrator-neuron mechanism, and the reproducible code repository. However, the quantitative comparison to the PID stack rests on an unvalidated internal attitude estimate and is reported without error bars or significance tests, so the strength of the 'estimation and control' claim is currently not supported by the measurements as presented.

major comments (3)
  1. [Section III-B, Table I] The headline RMSE comparison (3.03 degrees for the SNN vs. 2.67 degrees for the PID) is computed between the commanded roll setpoint and the 'resulting (estimated) roll angle'. For the SNN, this estimated roll is an internal, unsupervised intermediate representation: Section II-C.3 explains that the estimation sub-network has no access to control commands and is trained only through the merged control loss, and Section II-D states that OptiTrack provides only position and absolute heading, not full attitude. The paper therefore never checks the SNN's attitude estimate against ground truth. The internal variable could be a control-relevant latent that is not a physical attitude, in which case the reported RMSE does not measure attitude tracking error. Since the central claim is 'attitude estimation and control', the authors must either validate the estimate against true attitude (e.g., full-pose motion capture or a separate IMU-based ground-truth estimator) or explicitly narrow the claim to end-to-end control without asserting that the internal signal is an attitude estimate.
  2. [Table I and Section III-B] The comparison between controllers reports a single RMSE value per condition with no error bars, confidence intervals, or significance test, despite ten runs per controller. The 'average SD' in Table I is the standard deviation of the response traces across runs, not the variability of the RMSE; it says little about whether the 3.03 vs. 2.67 degree difference, or the differences among SNN variants (3.03, 3.10, 3.24, 3.14 degrees), are meaningful. The authors should report the per-run RMSE distribution (e.g., mean and standard deviation over the ten runs, or box plots) and, if appropriate, a paired test between the SNN and the PID stack. Without this, the abstract's quantitative comparison is not statistically supported.
  3. [Abstract, Section I, Section II-D, Section III-C] The manuscript repeatedly calls the system 'fully neuromorphic' (abstract and Section I). The experimental implementation runs on a Teensy 4.0, a conventional ARM microcontroller, as stated in Section II-D, and Section III-C explicitly acknowledges that 'The SNN in this research runs on a conventional microprocessor.' The contribution is a spiking algorithm deployed on non-neuromorphic hardware, which is valuable, but calling it a 'fully neuromorphic system' overstates the physical implementation. The authors should rephrase to indicate an all-SNN control pipeline executed on a conventional microcontroller, and reserve 'neuromorphic' for the algorithm or for hypothetical future hardware deployment.
minor comments (6)
  1. [Section II-C.1] The cross-correlation analysis that selects the time shift of approximately 6 steps should state explicitly whether it was performed on the training set only or on a held-out validation set, since this affects the risk of overfitting the reported flight results.
  2. [Section II-C.2] The abstract and introduction say the network maps 'raw sensory input directly to motor commands', but the network also receives attitude setpoints as inputs (Section II-B.2). This should be clarified so that 'raw' means 'without an explicit attitude estimator' rather than 'only sensor data'.
  3. [Table I] The rise-time (RT) metric is not defined in the text. Please specify the threshold used (e.g., 10% to 90% of the steady-state roll change) so the values in Table I are interpretable.
  4. [Section II-C.1] In the sentence 'a small delay in the derivative command will induce oscillations', the term 'derivative command' should likely be 'derivative component' or 'derivative action' for clarity.
  5. [Section III-C] The energy comparison relies on several assumptions (e.g., the 37x ratio between multiplication and addition energy, and the representation of the complementary filter cost as 15-30 multiplications). These estimates should be stated more cautiously, since the actual energy consumption depends on hardware-specific factors and the authors do not measure power directly.
  6. [Section IV] There is a typo in the conclusion: 'implicitely' should be 'implicitly'.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the central flight-performance result is an external benchmark against the standard PID stack, and the self-citations and data-driven time-shift are not load-bearing.

full rationale

The paper's central claim is empirical: an imitation-learned SNN maps raw IMU data to motor commands and flies a Crazyflie with tracking performance comparable to the standard PID/complementary-filter stack. This is evaluated in real flight against an external benchmark (Table I, Figure 4), not derived from the training objective by construction. The merging of the estimation and control sub-networks is a linear composition of weight matrices (Eqs. 3-4) and is not circular; the design explicitly avoids letting the estimator learn a trivial command-to-attitude shortcut (Section II-C.3). The time-shift augmentation (Section II-C.1) and fixed integrator neurons (Section II-C.4) are training procedures whose effects are measured by ablations in Table I, not by construction. The paper cites prior work by the same group ([21], [29]) for motivation and for the weight-combining trick, but the latter is elementary linear algebra and these citations are not load-bearing. The main validity caveat is that the 3.03-degree RMSE in Table I is computed against the SNN's own internal attitude estimate, with no OptiTrack ground truth on full attitude (Section II-D); this is a measurement-validity concern rather than circularity, because the command-versus-estimate error is not equal by definition to the training objective or to the true attitude error. Hence no circular step is exhibited, and the score is low.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The central result rests on no new physical entities. It depends on a standard spiking neuron model, on an imitation-learning assumption about the expert policy, and on several hand-set hyperparameters (time shift, surrogate slope, integrator parameters, disturbance statistics). The main unverified premise is that the estimation sub-network truly computes a generalizable attitude estimate; the paper does not measure the internal estimate against ground truth.

free parameters (6)
  • Target time shift = 6 timesteps (12 ms at 500 Hz)
    Chosen from the Pearson correlation peak between network output and PID target over the training set in Section II-C.1; the report calls it 'approximately 6 steps'.
  • Surrogate gradient slope s = 7
    Set by hand in Section II-C; the authors state the slope strongly influences training speed and final results.
  • Integrator neuron parameters (tau_syn, tau_mem, threshold) = 1, 1, 1 for 10 neurons in the control layer
    Fixed to create loss-free integration in Section II-C.4; the authors find this necessary for training stability.
  • Disturbance augmentation statistics = 1% probability per timestep, 0.2 s duration, U(0,50)% of maximum command
    Reality-gap augmentation hyperparameters in Section II-C.2, chosen without a reported tuning procedure.
  • Per-layer neuron counts = 150-150-130 before pruning, 150-100-80 after pruning
    Architecture sizes in Section II-D; pruning threshold chosen to retain over 99% of original MSE.
  • Loss weighting of Pearson correlation term = 0.5
    The loss in Eq. (5) is MSE + 0.5*(1 - Pearson); the relative weight is chosen by hand.
assumptions (6)
  • domain assumption The CUBA-LIF neuron model (Eqs. 1-2) used in training matches the behavior of the deployed Teensy implementation.
    Training is performed in a simulator of this neuron model; deployment assumes faithful execution without numerical or timing discrepancies. Section II-B.1 and II-D.
  • domain assumption The default Bitcraze complementary filter and cascaded PID constitute a valid expert policy for imitation learning.
    The training target is the output of this expert; if the expert were biased or unstable, the cloned policy would inherit the flaw. Section II-C.
  • standard math Merging the two sub-networks by linear weight multiplication (Eqs. 3-4) preserves the behavior of the separately trained networks.
    Valid because the output of the first network and the input of the second are linear transformations; the merge trick is taken from [29]. Section II-B.2.
  • domain assumption The internal attitude estimate of the estimation sub-network generalizes to states beyond the training distribution.
    No ground-truth evaluation of the estimator is provided; the paper only evaluates the full perception-to-control loop. Section II-C.3 and III-B.
  • standard math The surrogate gradient (Eq. 6) with slope s=7 provides a useful training signal for the spiking network.
    Standard approximation, but the slope is a hand-set hyperparameter that affects convergence. Section II-C.
  • domain assumption The 6-step target time shift makes the network learn a stable predictor rather than an unstable anticipatory controller.
    The delay compensation is validated empirically in flight, but no stability analysis of the closed loop with the shifted target is given. Section II-C.1.

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Pith. "Pith review of Neuromorphic Attitude Estimation and Control." pith.science (2026). https://pith.science/paper/HGRABPYK

@misc{pith2026241113945,
  author       = {Pith},
  title        = {Pith review of: Neuromorphic Attitude Estimation and Control},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HGRABPYK}},
  note         = {Machine review of arXiv:2411.13945}
}
read the original abstract

The real-world application of small drones is mostly hampered by energy limitations. Neuromorphic computing promises extremely energy-efficient AI for autonomous flight but is still challenging to train and deploy on real robots. To reap the maximal benefits from neuromorphic computing, it is necessary to perform all autonomy functions end-to-end on a single neuromorphic chip, from low-level attitude control to high-level navigation. This research presents the first neuromorphic control system using a spiking neural network (SNN) to effectively map a drone's raw sensory input directly to motor commands. We apply this method to low-level attitude estimation and control for a quadrotor, deploying the SNN on a tiny Crazyflie. We propose a modular SNN, separately training and then merging estimation and control sub-networks. The SNN is trained with imitation learning, using a flight dataset of sensory-motor pairs. Post-training, the network is deployed on the Crazyflie, issuing control commands from sensor inputs at 500Hz. Furthermore, for the training procedure we augmented training data by flying a controller with additional excitation and time-shifting the target data to enhance the predictive capabilities of the SNN. On the real drone, the perception-to-control SNN tracks attitude commands with an average error of 3.0 degrees, compared to 2.7 degrees for the regular flight stack. We also show the benefits of the proposed learning modifications for reducing the average tracking error and reducing oscillations. Our work shows the feasibility of performing neuromorphic end-to-end control, laying the basis for highly energy-efficient and low-latency neuromorphic autopilots.

Figures

Figures reproduced from arXiv: 2411.13945 by the authors.

Figure 1
Figure 1. We present an approach to training a spiking neural net [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Pearson Correlation between the output of the trained SNN [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Training loss curves comparing fixed versus free neuron leak [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Position step responses of the SNN system (top) and the [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Attitude step responses of A) the fully-trained SNN system, B) the SNN trained with augmentation, C) the SNN trained with time [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

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Works this paper leans on

47 extracted references · 33 canonical work pages

  1. [1]

    Applications of drone in disaster management: A scoping review,

    S. M. S. M. Daud, M. Y . P. M. Yusof, C. C. Heo, L. S. Khoo, M. K. C. Singh, M. S. Mahmood, and H. Nawawi, “Applications of drone in disaster management: A scoping review,” Science & Justice , vol. 62, no. 1, pp. 30–42, 2022

    2022

  2. [2]

    Drone remote sensing for forestry research and practices,

    L. Tang and G. Shao, “Drone remote sensing for forestry research and practices,” Journal of forestry research , vol. 26, pp. 791–797, 2015

    work page 2015

  3. [3]

    Review on application of drone systems in precision agriculture,

    U. R. Mogili and B. Deepak, “Review on application of drone systems in precision agriculture,” Procedia computer science, vol. 133, pp. 502– 509, 2018. 8 IEEE ROBOTICS AND AUTOMATION LETTERS. PREPRINT VERSION. ACCEPTED MARCH, 2025

    work page 2018

  4. [4]

    Reaching the limit in autonomous racing: Optimal control versus reinforcement learning,

    Y . Song, A. Romero, M. M ¨uller, V . Koltun, and D. Scaramuzza, “Reaching the limit in autonomous racing: Optimal control versus reinforcement learning,” Science Robotics, vol. 8, no. 82, p. eadg1462,

    work page

  5. [5]

    Drone deep reinforcement learning: A review,

    A. T. Azar, A. Koubaa, N. Ali Mohamed, H. A. Ibrahim, Z. F. Ibrahim, M. Kazim, A. Ammar, B. Benjdira, A. M. Khamis, I. A. Hameed et al., “Drone deep reinforcement learning: A review,” Electronics, vol. 10, no. 9, p. 999, 2021

    work page 2021

  6. [6]

    The limits and potentials of deep learning for robotics,

    N. S ¨underhauf, O. Brock, W. Scheirer, R. Hadsell, D. Fox, J. Leitner, B. Upcroft, P. Abbeel, W. Burgard, M. Milford et al. , “The limits and potentials of deep learning for robotics,” The International journal of robotics research, vol. 37, no. 4-5, pp. 405–420, 2018

    work page 2018

  7. [7]

    Neuromorphic computing hardware and neural architectures for robotics,

    Y . Sandamirskaya, M. Kaboli, J. Conradt, and T. Celikel, “Neuromorphic computing hardware and neural architectures for robotics,” Science Robotics, vol. 7, no. 67, p. eabl8419, 2022. [Online]. Available: https://www.science.org/doi/abs/10.1126/scirobotics.abl8419

    doi:10.1126/scirobotics.abl8419 2022

  8. [8]

    Event- based vision: A survey,

    G. Gallego, T. Delbr ¨uck, G. Orchard, C. Bartolozzi, B. Taba, A. Censi, S. Leutenegger, A. J. Davison, J. Conradt, K. Daniilidis et al., “Event- based vision: A survey,” IEEE transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 44, no. 1, pp. 154–180, 2020

    work page 2020

Show all 47 references
  1. [9]

    A 128 ×128 120 db 15 µs latency asynchronous temporal contrast vision sensor,

    P. Lichtsteiner, C. Posch, and T. Delbruck, “A 128 ×128 120 db 15 µs latency asynchronous temporal contrast vision sensor,” IEEE Journal of Solid-State Circuits, vol. 43, no. 2, pp. 566–576, 2008

    2008

  2. [10]

    Neuromorphic vision sensors,

    G. Indiveri and R. Douglas, “Neuromorphic vision sensors,” Science, vol. 288, no. 5469, pp. 1189–1190, 2000

    2000

  3. [11]

    Networks of spiking neurons: the third generation of neural network models,

    W. Maass, “Networks of spiking neurons: the third generation of neural network models,” Neural Networks, vol. 10, no. 9, pp. 1659–1671, 1997

    1997

  4. [12]

    Haltere–mediated equilibrium reflexes of the fruit fly, drosophila melanogaster,

    M. H. Dickinson, “Haltere–mediated equilibrium reflexes of the fruit fly, drosophila melanogaster,” Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences , vol. 354, no. 1385, pp. 903–916, 1999

    1999

  5. [13]

    Embodied neuromorphic intelligence,

    C. Bartolozzi, G. Indiveri, and E. Donati, “Embodied neuromorphic intelligence,” Nature Communications, vol. 13, no. 1, p. 1024, 2022

    2022

  6. [14]

    A survey of neuromorphic computing and neural networks in hardware,

    C. D. Schuman, T. E. Potok, R. M. Patton, J. D. Birdwell, M. E. Dean, G. S. Rose, and J. S. Plank, “A survey of neuromorphic computing and neural networks in hardware,” arXiv preprint arXiv:1705.06963 , 2017

    2017 arXiv

  7. [15]

    End-to-end learned event-and image- based visual odometry,

    R. Pellerito, M. Cannici, D. Gehrig, J. Belhadj, O. Dubois-Matra, M. Casasco, and D. Scaramuzza, “End-to-end learned event-and image- based visual odometry,” arXiv preprint arXiv:2309.09947 , 2023

    2023 arXiv

  8. [16]

    Towards low-latency high-bandwidth control of quadrotors using event cameras,

    R. S. Dimitrova, M. Gehrig, D. Brescianini, and D. Scaramuzza, “Towards low-latency high-bandwidth control of quadrotors using event cameras,” in 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020, pp. 4294–4300

    2020

  9. [17]

    Event-driven vision and control for uavs on a neuromorphic chip,

    A. Vitale, A. Renner, C. Nauer, D. Scaramuzza, and Y . Sandamirskaya, “Event-driven vision and control for uavs on a neuromorphic chip,” in 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021, pp. 103–109

    2021

  10. [18]

    Dynamic obstacle avoidance for quadrotors with event cameras,

    D. Falanga, K. Kleber, and D. Scaramuzza, “Dynamic obstacle avoidance for quadrotors with event cameras,” Science Robotics , vol. 5, no. 40, p. eaaz9712, 2020. [Online]. Available: https: //www.science.org/doi/abs/10.1126/scirobotics.aaz9712

    2020 doi

  11. [19]

    Champion-level drone racing using deep reinforcement learning,

    E. Kaufmann, L. Bauersfeld, A. Loquercio, M. M ¨uller, V . Koltun, and D. Scaramuzza, “Champion-level drone racing using deep reinforcement learning,” Nature, vol. 620, no. 7976, pp. 982–987, 2023

    2023

  12. [20]

    Towards neuromorphic control: A spiking neural network based pid controller for uav,

    R. Stagsted, A. Vitale, J. Binz, L. Bonde Larsen, Y . Sandamirskaya et al., “Towards neuromorphic control: A spiking neural network based pid controller for uav,” in Robotics: Science and Systems 2020, Virtual Conference. RSS, 2020

    2020

  13. [21]

    Design and imple- mentation of a parsimonious neuromorphic pid for onboard altitude control for mavs using neuromorphic processors,

    S. Stroobants, J. Dupeyroux, and G. De Croon, “Design and imple- mentation of a parsimonious neuromorphic pid for onboard altitude control for mavs using neuromorphic processors,” in Proceedings of the International Conference on Neuromorphic Systems 2022 , 2022, pp. 1–7

    2022

  14. [22]

    Spiking neural network (snn) control of a flapping insect-scale robot,

    T. S. Clawson, S. Ferrari, S. B. Fuller, and R. J. Wood, “Spiking neural network (snn) control of a flapping insect-scale robot,” in 2016 IEEE 55th Conference on Decision and Control (CDC) . IEEE, 2016, pp. 3381–3388

    2016

  15. [23]

    Evolving spiking neurocontrollers for uavs,

    H. Qiu, M. Garratt, D. Howard, and S. Anavatti, “Evolving spiking neurocontrollers for uavs,” in 2020 IEEE Symposium Series on Compu- tational Intelligence (SSCI) . IEEE, 2020, pp. 1928–1935

    2020

  16. [24]

    Control of a quadrotor with reinforcement learning,

    J. Hwangbo, I. Sa, R. Siegwart, and M. Hutter, “Control of a quadrotor with reinforcement learning,” IEEE Robotics and Automation Letters , vol. 2, no. 4, pp. 2096–2103, 2017

    2017

  17. [25]

    Deep Drone Acrobatics,

    E. Kaufmann, A. Loquercio, R. Ranftl, M. M ¨uller, V . Koltun, and D. Scaramuzza, “Deep Drone Acrobatics,” in Proceedings of Robotics: Science and Systems , Corvalis, Oregon, USA, July 2020

    2020

  18. [26]

    End-to-end reinforcement learning for time-optimal quadcopter flight,

    R. Ferede, C. De Wagter, D. Izzo, and G. C. De Croon, “End-to-end reinforcement learning for time-optimal quadcopter flight,” in2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 6172–6177

    2024

  19. [27]

    Learning deep control policies for autonomous aerial vehicles with mpc-guided policy search,

    T. Zhang, G. Kahn, S. Levine, and P. Abbeel, “Learning deep control policies for autonomous aerial vehicles with mpc-guided policy search,” in 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016, pp. 528–535

    2016

  20. [28]

    Loihi: A neuromorphic manycore processor with on-chip learning,

    M. Davies, N. Srinivasa, T.-H. Lin, G. Chinya, Y . Cao, S. H. Choday, G. Dimou, P. Joshi, N. Imam, S. Jain et al. , “Loihi: A neuromorphic manycore processor with on-chip learning,” Ieee Micro, vol. 38, no. 1, pp. 82–99, 2018

    2018

  21. [29]

    Fully neuromorphic vision and control for autonomous drone flight,

    F. Paredes-Vall ´es, J. J. Hagenaars, J. Dupeyroux, S. Stroobants, Y . Xu, and G. C. H. E. de Croon, “Fully neuromorphic vision and control for autonomous drone flight,” Science Robotics , vol. 9, no. 90, p. eadi0591, 2024. [Online]. Available: https://www.science.org/doi/abs/...

    2024 doi

  22. [30]

    Closed-form control with spike coding networks,

    F. S. Slijkhuis, S. W. Keemink, and P. Lanillos, “Closed-form control with spike coding networks,” IEEE Transactions on Cognitive and Developmental Systems, 2023

    2023

  23. [31]

    The true role of accelerometer feedback in quadrotor control,

    P. Martin and E. Sala ¨un, “The true role of accelerometer feedback in quadrotor control,” in 2010 IEEE International Conference on Robotics and Automation. IEEE, 2010, pp. 1623–1629

    2010

  24. [32]

    Crazyflie 2.0 quadrotor as a platform for research and education in robotics and control engineering,

    W. Giernacki, M. Skwierczy ´nski, W. Witwicki, P. Wro ´nski, and P. Kozierski, “Crazyflie 2.0 quadrotor as a platform for research and education in robotics and control engineering,” in 2017 22nd Interna- tional Conference on Methods and Models in Automation and Robotics (MMAR...

    2017

  25. [33]

    Cohen, Y

    I. Cohen, Y . Huang, J. Chen, J. Benesty, J. Benesty, J. Chen, Y . Huang, and I. Cohen, Pearson Correlation Coefficient . Berlin, Heidelberg: Springer, 2009, pp. 1–4

    2009

  26. [34]

    Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based opti- mization to spiking neural networks,

    E. O. Neftci, H. Mostafa, and F. Zenke, “Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based opti- mization to spiking neural networks,”IEEE Signal Processing Magazine, vol. 36, no. 6, pp. 51–63, 2019

    2019

  27. [35]

    Incorporating learnable membrane time constant to enhance learning of spiking neural networks,

    W. Fang, Z. Yu, Y . Chen, T. Masquelier, T. Huang, and Y . Tian, “Incorporating learnable membrane time constant to enhance learning of spiking neural networks,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 2661–2671

    2021

  28. [36]

    The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks,

    F. Zenke and T. P. V ogels, “The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks,” Neural Computation, vol. 33, no. 4, pp. 899–925, 2021

    2021

  29. [37]

    A reduction of imitation learning and structured prediction to no-regret online learning,

    S. Ross, G. Gordon, and D. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning,” in Proceedings of the fourteenth International Conference on Artificial Intelligence and Statistics. JMLR Workshop and Conference Proceedings, 2011, ...

    2011

  30. [38]

    Learning monocular reactive uav control in cluttered natural environments,

    S. Ross, N. Melik-Barkhudarov, K. S. Shankar, A. Wendel, D. Dey, J. A. Bagnell, and M. Hebert, “Learning monocular reactive uav control in cluttered natural environments,” in 2013 IEEE International Conference on Robotics and Automation . IEEE, 2013, pp. 1765–1772

    2013

  31. [39]

    Resurrecting recurrent neural networks for long sequences,

    A. Orvieto, S. L. Smith, A. Gu, A. Fernando, C. Gulcehre, R. Pascanu, and S. De, “Resurrecting recurrent neural networks for long sequences,” in International Conference on Machine Learning . PMLR, 2023, pp. 26 670–26 698

    2023

  32. [40]

    Deep state space models for time series forecasting,

    S. S. Rangapuram, M. W. Seeger, J. Gasthaus, L. Stella, Y . Wang, and T. Januschowski, “Deep state space models for time series forecasting,” Advances in neural information processing systems , vol. 31, 2018

    2018

  33. [41]

    Avoidbench: A high-fidelity vision-based obstacle avoidance benchmarking suite for multi-rotors,

    H. Yu, G. C. E. de Croon, and C. De Wagter, “Avoidbench: A high-fidelity vision-based obstacle avoidance benchmarking suite for multi-rotors,” in 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023, pp. 9183–9189

    2023

  34. [42]

    Efficient neuromorphic signal processing with loihi 2,

    G. Orchard, E. P. Frady, D. B. D. Rubin, S. Sanborn, S. B. Shrestha, F. T. Sommer, and M. Davies, “Efficient neuromorphic signal processing with loihi 2,” in 2021 IEEE Workshop on Signal Processing Systems (SiPS) . IEEE, 2021, pp. 254–259

    2021

  35. [43]

    The spinnaker project,

    S. B. Furber, F. Galluppi, S. Temple, and L. A. Plana, “The spinnaker project,” Proceedings of the IEEE , vol. 102, no. 5, pp. 652–665, 2014

    2014

  36. [44]

    Addition is all you need for energy-efficient language models,

    H. Luo and W. Sun, “Addition is all you need for energy-efficient language models,” arXiv preprint arXiv:2410.00907 , 2024

    2024 arXiv

  37. [45]

    Attitude stabi- lization of a quadrotor by means of event-triggered nonlinear control,

    J.-F. Guerrero-Castellanos, J. J. T ´ellez-Guzm´an, S. Durand, N. Marc- hand, J. U. Alvarez-Mu ˜noz, and V . R. Gonzalez-Diaz, “Attitude stabi- lization of a quadrotor by means of event-triggered nonlinear control,” Journal of Intelligent & Robotic Systems , vol. 73, pp. 123–135, 2014

    2014

  38. [46]

    Bootstrapping reinforcement learning with imitation for vision-based agile flight,

    J. Xing, A. Romero, L. Bauersfeld, and D. Scaramuzza, “Bootstrapping reinforcement learning with imitation for vision-based agile flight,” arXiv preprint arXiv:2403.12203 , 2024

    2024 arXiv

  39. [2023]

    Available: https://www.science.org/doi/abs/10.1126/ scirobotics.adg1462

    [Online]. Available: https://www.science.org/doi/abs/10.1126/ scirobotics.adg1462

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.