A Talent-infused Policy-gradient Approach to Efficient Co-Design of Morphology and Task Allocation Behavior of Multi-Robot Systems

Prajit KrisshnaKumar; Souma Chowdhury; Steve Paul

REVIEW 4 major objections 6 minor 34 references

A Talent-infused Policy-gradient Approach to Efficient Co-Design of Morphology and Task Allocation Behavior of Multi-Robot Systems

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Robot teams can be co-designed by first mapping physical design to a compact set of capability 'talent' metrics, then learning behavior and talent trade-offs jointly, a two-stage decomposition that is nearly lossless and that outperforms…

desk verdict Solid empirical co-design result undercut by an unproven 'nearly lossless' claim; worth a serious referee but needs qualification or a direct test. read the letter →

arxiv 2411.18519 v1 pith:6NOR2PKM submitted 2024-11-27 cs.RO cs.MA

Prajit KrisshnaKumar , Steve Paul , Souma Chowdhury This is my paper · ORCID

classification cs.ROcs.MA

keywords co-designmorphology-behaviorco-optimizationmulti-robottaskallocationtalentmetricsParetofrontpolicygradientgraphreinforcementlearningfloodresponse

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

Multi-robot performance depends on both the physical design of each robot and the learned behavior that coordinates them, and this paper tries to optimize the two together instead of sequentially. Its central claim is that the co-design problem can be split into two stages with almost no loss: first find the Pareto front of morphology-dependent capability metrics (maximum flight range, maximum speed, package capacity), then use a talent-infused policy-gradient learner to select the best talent trade-off and behavior simultaneously subject to that front. A sympathetic reader should care because this makes a computationally heavy nested co-design problem tractable, roughly 10 hours instead of an estimated 272 hours in the paper's UAV flood-response case, and because the resulting co-designed team outperforms sequential-design baselines in task completion, with the advantage surviving scaling from 50 to 150 tasks. The framework also lets the authors compare a co-designed multi-robot team against a co-designed single robot with a much larger morphology budget, showing distinct morphology and behavior trade-offs and a multi-robot advantage that appears as task numbers grow.

What carries the argument

The load-bearing object is the talent metric vector $Y_{TL} = f_M(X_M)$, a small set of robot capabilities that must satisfy four principles: be functions of morphology alone, have a consistent direction of goodness, be collectively sufficient for computing the robot's state transitions, and conflict on part of the design space. In this paper the talents are maximum flight range, nominal speed, and package capacity, mapped from UAV arm length and width, motor, battery, propeller, and payload choices. The framework first solves a multi-objective optimization to build the talent Pareto front, the set of best trade-off capability combinations, and models it with a polynomial response surface plus conditional quantile regressions that set feasible bounds on each talent given the previous ones. Then a talent-infused actor-critic policy is trained: a small bias-only talent network with no inputs produces the talent values, a decoder scales them to the Pareto-constrained bounds, the final talent is obtained from the surrogate, and the critic evaluates state-talent values so the gradient updates both behavioral actions and talent choices together. This two-stage machinery is what carries the near-lossless decomposition and the computational savings.

What would settle it

Run the flood-response simulation twice with the same learned talents (range, speed, capacity) but different underlying morphologies that realize those talents, for example one with a high-power motor and fast charging versus one with a low-power motor and slow charging, and compare task completion; if behavior or the optimal policy changes, the talent set is not sufficient and the nearly lossless decomposition fails.

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Extended reading notes

Core claim

The discovery the paper aims to establish is that morphology-behavior co-design for multi-robot teams can be decomposed, nearly losslessly, into a talent-Pareto search and a talent-behavior policy optimization. The talent metrics $Y_{TL}$ are morphology-dependent capabilities that are collectively sufficient to determine the MDP state transitions of a robot, so the robot in simulation can be embodied by its talents alone without explicitly instantiating morphology. Under the assumption of ideal search in each stage, any solution of the original nested co-design problem exists in the space spanned by the decomposed approach. Empirically, the talent-infused policy-gradient co-design reaches a median task completion of about 90 percent in the 50-task flood-response environment, compared with about 83 percent for two sequential baselines whose talents were also chosen from the Pareto front, and the advantage persists when tasks and robots scale to 100 and 150. The co-designed multi-robot policy also differs meaningfully from a co-designed single-robot policy allowed a much larger morphology envelope: the single robot favors speed, performs best only at small task scales, and drops below 50 percent completion when the task count triples.

Load-bearing premise

The decomposition stands or falls on the assumption that the three talent metrics, maximum flight range, maximum speed, and package capacity, are collectively sufficient, meaning no change in underlying morphology such as motor, propeller, arm dimensions, charging, or energy draw can alter feasible behavior or state transitions once these three numbers are fixed.

Editorial extensions

If this is right

If the decomposition is as lossless as claimed, morphology and behavior can be co-designed without running a nested optimization over the full morphology space, turning a 272-hour search into roughly 10 hours in this UAV case.
Co-designed policies will outperform sequential designs that fix morphology or talents before learning behavior, with the gap persisting as the task and team scale grow, at least across the 50, 100, and 150-task regimes tested.
Because talents, not morphology, enter the MDP, the same trained policy can be transferred to any physical embodiment whose capabilities map to the same talent values, enabling reuse across designs.
Comparing multi-robot and single-robot co-design reveals that a single robot with a far larger morphology budget is competitive only at small task scales; multi-robot teams become necessary as task count grows, a scaling insight that follows from the co-designed behavior.

Reading between the lines

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

Beyond the paper: the near-losslessness claim is conditional on ideal search in each stage, so a concrete extension is to quantify how much approximation error the polynomial Pareto surrogate and quantile bounds introduce relative to the true feasible talent region.
A natural extension would test the collective-sufficiency principle directly by keeping range, speed, and capacity fixed while varying hidden morphology parameters such as motor power, propeller size, or charging behavior, and checking whether optimal task-allocation behavior changes; if it does, the talent set needs more dimensions.
The framework suggests a reusable pipeline: given a morphology family and a task-allocation MDP, the talent Pareto front can be built once and the talent-infused policy can then be re-trained cheaply for new task distributions, amortizing the initial multi-objective search.
An implicit prediction is that the multi-robot advantage over a single capable robot should have a measurable threshold in task count and environment size; future work could map that threshold as a function of the talent Pareto boundary, helping designers choose between one sophisticated robot and a team of simpler ones.

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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,

The paper delivers a credible empirical result: talent-infused policy-gradient co-design outperforms two fixed-talent sequential baselines on an MRTA flood-response task, across three scales, and the advantage holds on unseen episodes. That part is solid.

What is genuinely new: the talent network inside the actor, which outputs talent values that are fixed per episode and constrained by a Pareto front learned offline, is a neat idea. It extends the authors' prior talent-metric work and the graph RL policy into a co-design tool that also cleanly compares single vs multi-robot designs. The efficiency gain (10 hours vs an estimated 272 for nested co-design) is compelling if the decomposition is genuinely near-lossless.

But that 'nearly lossless' claim is the soft spot. It is asserted in Section II, not demonstrated. The experiments never run a nested optimizer; the 272-hour figure is an estimate. The talent sufficiency principle (Principle 3) is also just stated. The three talents—range, speed, and package capacity—may not capture every morphology effect on the MDP transition, and the simulation's linear charging and identical-UAV assumptions make that compression easier than it would be in a real system. The quadratic surrogate for package capacity is fit to 289 NSGA-II points with no reported validation. These are real gaps, but they do not sink the main empirical comparison, which is against fixed-talent baselines from the same Pareto front, not against a full nested search. The paper would be strengthened by either a downscaled brute-force comparison or an explicit sensitivity study, and by toning down 'nearly lossless' to 'approximately lossless under the stated assumptions.'

The paper is clearly written, the methodology is reproducible in principle, and the authors acknowledge the talent-sufficiency limitation in the conclusion. It deserves a serious referee; I would send it out. It is a subfield gain, not a paradigm shift, but the result is useful and the single-vs-multi-robot analysis is a nice addition.

Recommendation: accept with revisions, provided the losslessness claim is qualified or tested.

Referee Report

4 major / 6 minor

Summary. The paper proposes a co-design framework for multi-robot systems that jointly optimizes morphology and learned task-allocation behavior. The key idea is to compress morphology into a small set of "talent" metrics (max flight range, max speed, package capacity), construct a Pareto front over these talents using NSGA-II, and then train a talent-infused policy-gradient actor-critic in which the talent values are also optimized while a policy network selects tasks. The paper claims this decomposition is "nearly lossless" relative to a brute-force nested co-design, reports that the co-designed policy outperforms two fixed-talent sequential-design baselines on the MRTA-Flood scenario across 50, 100, and 150 task scales, and compares co-designed multi-robot versus single-robot designs. It also reports a total co-design cost of about 10 hours versus an estimated 272 hours for nested co-design.

Significance. If the central decomposition claim held, the talent-Pareto-plus-policy-gradient approach would be a meaningful step toward practical morphology-behavior co-design for multi-robot systems, since it replaces an expensive nested search with a sequence of cheaper problems. The empirical comparison against two hand-picked Pareto-front baselines over 250 episodes at three scales is a real, if narrow, result: it supports the claim that the learned talents and behavior outperform two specific fixed-talent designs, and this evidence is presented with concrete task-completion statistics. The paper also has a useful comparative insight that co-designed multi-robot systems and co-designed single robots select different morphology trade-offs. However, the headline "nearly lossless" claim is asserted rather than demonstrated, and the talent-sufficiency assumption on which the decomposition rests is not validated; these are load-bearing gaps for the main contribution of the paper.

major comments (4)

[Section II, opening paragraph] The central claim that the decomposition is "nearly lossless" (Section II) is asserted without proof or experimental validation. The paper does not run a nested co-design baseline, which is the comparator named in the claim. The experiments in Section IV-C compare only against two fixed-talent sequential-design baselines, which are strictly weaker than the brute-force nested optimizer in Eq. (1). A downscaled brute-force comparison (using a coarser morphology discretization or a smaller problem instance) is needed to establish that the decomposed search space in Eq. (3) actually retains near-optimal solutions of the original problem; otherwise the efficiency claim is only an upper bound on cost, not an equivalence statement.
[Section II-A, Principle 3] The talent-sufficiency condition is stated as an axiom but never tested. The paper asserts that max range, max speed, and package capacity are collectively sufficient to determine all state transitions of the MDP, yet the flood-response simulation uses a linear 50-minute charging model and identical UAVs (Section III). If a morphology change altered, for example, energy consumption at partial payload, recharge dynamics, or turnaround time while leaving range, speed, and capacity fixed, then fixed Y_TL would not determine the state transition, and the decomposition could be lossy. The conclusion acknowledges this limitation in words, but no experiment or sensitivity analysis checks whether the three chosen talents are sufficient for the simulated mission. This is load-bearing because Principle 3 is the bridge between morphology space and talent space.
[Section II-B and Section IV-A] The quadratic surrogate for package capacity (Eq. (5), fit to 289 NSGA-II points) is used to define the feasible talent boundary in Eq. (8), but no goodness-of-fit or validity check is reported. An inaccurate surrogate could either admit infeasible talent combinations or exclude feasible ones, directly undermining the claim that the talent Pareto boundary correctly represents the achievable morphology space. The paper should report the surrogate error (e.g., R-squared, mean absolute error, or a holdout cross-validation) and show that the quantile-regression bounds in Eq. (3) actually contain the true feasible talent region.
[Section IV-C and Figures 6-7] The empirical comparison reports medians and box plots but no statistical significance tests or confidence intervals. Given that the claimed advantage over baselines is on the order of 7 percentage points, it is important to know whether the difference is stable across seeds and episodes; the current presentation does not distinguish systematic improvement from sampling variability. Adding a repeated-seed evaluation or a paired significance test would strengthen the narrower claim that learned talents beat hand-picked Pareto talents.

minor comments (6)

[Section II-D, Eq. (9)] The bounds in Eq. (9) are written as Xmin ≤ XM ≤ Xmin; the upper bound should presumably be Xmax. This is a typo, but it matters for the morphology-finalization step.
[Section II-C, Eq. (6)] The notation for talent values is inconsistent: the text uses ^Y_TL,1,...,^Y_TL,m-1, while Eq. (6) writes ^Y_TL,2 with a space, and subsequent equations mix Y_TL and ^Y_TL. Please unify the notation and clarify which variables are raw network outputs and which are scaled talent values.
[Section II-C] The sentence "a forward pass in the actor network (consisting of both the talent network and behavior network), followed by sampling from the distribution" does not explain whether the Gaussian distribution is over behavioral actions only or over the concatenated action-talent vector. The later statement that talents are not sampled after the first step suggests the former, but the text should say so explicitly.
[Section IV-B] The reported "final cumulative standard deviation of the policy narrows down to 6.9%" is ambiguous. It is unclear whether this refers to the standard deviation of the behavioral action distribution, of the talent outputs, or of the reward across training. Please state the quantity precisely.
[Section IV-E and Table II] The single-robot case studies in Section IV-E compare co-design against only one fixed-talent baseline, whereas the multi-robot case uses two baselines. Since the single-robot comparison is used to draw conclusions about when multi-robot teams become beneficial, the absence of a second baseline and of variability information should be acknowledged when interpreting these results.
[Throughout] The paper contains several typographical and formatting issues, including inconsistent spacing in equations (e.g., "Y_TL, 2" in Eq. (6)) and incomplete sentence fragments in the captions of Figures 4 and 5. A careful proofread is needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's co-design decomposition, empirical comparison, and efficiency estimates do not reduce to their inputs by construction or via load-bearing self-citation.

full rationale

The central empirical claim is the co-designed policy outperforming fixed-talent sequential-design baselines on unseen task instances (Section IV-C, Fig. 6). That comparison is not circular: the baselines are trained separately with fixed talents chosen from the same Pareto front, and the evaluation environment is randomly regenerated. The talent-Pareto boundary is fit to NSGA-II evaluations of the morphology-to-talent map fM (Section IV-A), and the talent-infused actor-critic then selects a point on that boundary; the final morphology is recovered by a separate inversion (Eq. 9). No fitted parameter is renamed as a prediction: the quadratic package-capacity surrogate is used inside the optimization loop, not as an evaluated performance metric. The paper's 'nearly lossless' claim in Section II ('the decomposition approach presented here is nearly lossless, i.e., a solution that can be found otherwise with a brute-force nested optimization approach to co-design will also exist in the overall search space spanned by our decomposed co-design approach') is an asserted conditional, not a derived equation. It depends on the unverified talent-sufficiency Principle 3 in Section II-A ('there cannot be a case where constraints or bounds on behavior can change with a fixed value of Y_TL'), and the authors themselves flag this limitation in the Conclusion: 'In its current form, the talent metrics must be purely functions of morphology, as well as be collectively sufficient to simulate the state transition underlying the robot behavior, which might be challenging to apply in settings with more complex robot/environment interactions.' An unproven assumption is a soundness or validity risk, not circularity. Self-citations [22], [23], [27], [28] supply the talent concept and the graph-policy architecture from prior work, but the present contribution—the decomposition formulation, the talent-infused policy-gradient extension, and the empirical study—does not reduce to those citations. No equation in the paper is shown to be equivalent to an input by construction, and no reported result is statistically forced by a fitted parameter.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central claims rely on the talent sufficiency assumption, the near-losslessness assertion, the accuracy of the Pareto and quantile surrogates, and simplified UAV/environment modeling. No code or data are shipped, so the fitted surrogate parameters and policy hyperparameters are not independently auditable.

free parameters (2)

Pareto front polynomial coefficients = not reported
Quadratic regression for package capacity as a function of range and speed, fit to 289 NSGA-II Pareto solutions; the resulting boundary constrains the talent-behavior optimization and its approximation error is not reported.
Quantile regression bounds = not reported
5th and 95th percentile speed bounds conditional on range, fit to the same Pareto data; used in Eq. 8 to scale and constrain the second talent during learning.

assumptions (4)

domain assumption Talent metrics are solely functions of morphology and are collectively sufficient to compute state transitions of the robot behavior model.
Section II-A principles 1 and 3; without sufficiency, restricting the search to the talent Pareto front can exclude optimal morphologies.
ad hoc to paper The optimal solution of the original co-design problem is retained in the decomposed search space (near-losslessness).
Section II, after Eq. 2; stated with 'assuming each search process is ideal' but no formal equivalence proof.
domain assumption The NSGA-II Pareto front and polynomial/quantile surrogates accurately represent the feasible talent space.
Section IV-A; 289 Pareto solutions from six runs; no validation of surrogate error.
domain assumption All UAVs are identical, tasks/deadlines are known, and recharging is linear with a fixed 50-minute empty-to-full time.
Section III, assumptions 1-4; these simplify the simulation and may not hold in real flood response.

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Cite this review

Pith. "Pith review of A Talent-infused Policy-gradient Approach to Efficient Co-Design of Morphology and Task Allocation Behavior of Multi-Robot Systems." pith.science (2026). https://pith.science/paper/6NOR2PKM

@misc{pith2026241118519,
  author       = {Pith},
  title        = {Pith review of: A Talent-infused Policy-gradient Approach to Efficient Co-Design of Morphology and Task Allocation Behavior of Multi-Robot Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6NOR2PKM}},
  note         = {Machine review of arXiv:2411.18519}
}

read the original abstract

Interesting and efficient collective behavior observed in multi-robot or swarm systems emerges from the individual behavior of the robots. The functional space of individual robot behaviors is in turn shaped or constrained by the robot's morphology or physical design. Thus the full potential of multi-robot systems can be realized by concurrently optimizing the morphology and behavior of individual robots, informed by the environment's feedback about their collective performance, as opposed to treating morphology and behavior choices disparately or in sequence (the classical approach). This paper presents an efficient concurrent design or co-design method to explore this potential and understand how morphology choices impact collective behavior, particularly in an MRTA problem focused on a flood response scenario, where the individual behavior is designed via graph reinforcement learning. Computational efficiency in this case is attributed to a new way of near exact decomposition of the co-design problem into a series of simpler optimization and learning problems. This is achieved through i) the identification and use of the Pareto front of Talent metrics that represent morphology-dependent robot capabilities, and ii) learning the selection of Talent best trade-offs and individual robot policy that jointly maximizes the MRTA performance. Applied to a multi-unmanned aerial vehicle flood response use case, the co-design outcomes are shown to readily outperform sequential design baselines. Significant differences in morphology and learned behavior are also observed when comparing co-designed single robot vs. co-designed multi-robot systems for similar operations.

Figures

Figures reproduced from arXiv: 2411.18519 by the authors.

**Figure 2.** The overall policy network consists of the GCAPCN encoder, context encoding, the MHA-based decoder, and Talent [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Talent Pareto front approximated by polynomial regression; limits of talents captured with quantile regression. a) [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Training history for MRTA co-design policy (Talents [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

**Figure 5.** Figure 5: Training history for Single Robot Task Allocation [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: Multi-Robot Case: Task completion rate of co [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

**Figure 7.** Figure 7: Single Robot Case: Task completion rate of co [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

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S. Chowdhury, W. Tong, A. Messac, and J. Zhang, “A mixed- discrete particle swarm optimization algorithm with explicit diversity- preservation,” Structural and Multidisciplinary Optimization , vol. 47, pp. 367–388, 2013

2013
[30]

Multi-robot task allocation in disaster response: Addressing dynamic tasks with deadlines and robots with range and payload constraints,

P. Ghassemi and S. Chowdhury, “Multi-robot task allocation in disaster response: Addressing dynamic tasks with deadlines and robots with range and payload constraints,” Robotics and Autonomous Systems , p. 103905, 2021

2021
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Efficient planning of multi-robot collective transport using graph reinforcement learning with higher order topological abstraction,

S. Paul, W. Li, B. Smyth, Y . Chen, Y . Gel, and S. Chowdhury, “Efficient planning of multi-robot collective transport using graph reinforcement learning with higher order topological abstraction,” in 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, ...

2023
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Graph capsule convolutional neural networks,

S. Verma and Z. L. Zhang, “Graph capsule convolutional neural networks,” 2018

2018
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A scalable graph learning approach to capacitated vehicle routing problem using capsule networks and attention mechanism,

S. Paul and S. Chowdhury, “A scalable graph learning approach to capacitated vehicle routing problem using capsule networks and attention mechanism,” in International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol. V olume...

2022 doi
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Stable-baselines3: Reliable reinforcement learning implementations,

A. Ra ffin, A. Hill, A. Gleave, A. Kanervisto, M. Ernestus, and N. Dormann, “Stable-baselines3: Reliable reinforcement learning implementations,” Journal of Machine Learning Research , vol. 22, no. 268, pp. 1–8, 2021. [Online]. Available: http: //jmlr.org/papers/ v22/20-1364.html

2021

Pith tools

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

[1] [1]

Securing emergent behaviour in swarm robotics,

L. Chen and S.-L. Ng, “Securing emergent behaviour in swarm robotics,” Journal of Information Security and Applications , vol. 64, p. 103047, 2022

work page 2022

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Concurrent design of control software and configuration of hardware for robot swarms under economic constraints,

M. Salman, A. Ligot, and M. Birattari, “Concurrent design of control software and configuration of hardware for robot swarms under economic constraints,” PeerJ Computer Science, vol. 5, p. e221, 2019

work page 2019

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Automatic o ff-line design of robot swarms: a manifesto,

M. Birattari, A. Ligot, D. Bozhinoski, M. Brambilla, G. Francesca, L. Garattoni, D. Garz ´on Ramos, K. Hasselmann, M. Kegeleirs, J. Kuckling et al. , “Automatic o ff-line design of robot swarms: a manifesto,” Frontiers in Robotics and AI , vol. 6, p. 59, 2019

work page 2019

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Automode-mate: Automatic o ff-line design of spatially- organizing behaviors for robot swarms,

F. J. Mendiburu, D. G. Ramos, M. R. Morais, A. M. Lima, and M. Birattari, “Automode-mate: Automatic o ff-line design of spatially- organizing behaviors for robot swarms,” Swarm and Evolutionary Computation, vol. 74, p. 101118, 2022

work page 2022

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Toward an empirical practice in o ffline fully automatic design of robot swarms,

A. Ligot, A. Cotorruelo, E. Garone, and M. Birattari, “Toward an empirical practice in o ffline fully automatic design of robot swarms,” IEEE transactions on evolutionary computation , vol. 26, no. 6, pp. 1236–1245, 2022

work page 2022

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Automode-icepop: automatic modular design of control software for robot swarms using simulated annealing,

J. Kuckling, K. Ubeda Arriaza, and M. Birattari, “Automode-icepop: automatic modular design of control software for robot swarms using simulated annealing,” in Artificial Intelligence and Machine Learning: 31st Benelux AI Conference, BNAIC 2019, and 28th Belgian-Dutch Machine Learning Conference, BENELEARN 2019, Brussels, Belgium, November 6-8, 2019, Revi...

work page 2019

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Automatic design of communication-based behaviors for robot swarms,

K. Hasselmann, F. Robert, and M. Birattari, “Automatic design of communication-based behaviors for robot swarms,” in Swarm In- telligence: 11th International Conference, ANTS 2018, Rome, Italy, October 29–31, 2018, Proceedings 11 . Springer, 2018, pp. 16–29

work page 2018

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An experiment in automatic design of robot swarms: Automode- vanilla, evostick, and human experts,

G. Francesca, M. Brambilla, A. Brutschy, L. Garattoni, R. Miletitch, G. Podevijn, A. Reina, T. Soleymani, M. Salvaro, C. Pinciroli et al., “An experiment in automatic design of robot swarms: Automode- vanilla, evostick, and human experts,” in Swarm Intelligence: 9th International Conference, ANTS 2014, Brussels, Belgium, September 10-12, 2014. Proceedings...

work page 2014