Julia Ebert, Melvin Gauci, Frederick Mallmann-Trenn, and Radhika Nagpal. 2020. “Bayes Bots: Collective Bayesian Decision-Making in Decentralized Robot Swarms.” Intl. Conference on Robotics and Automation (ICRA). icra2020-ebert.pdf
Melinda Malley, Bahar Haghighat, Lucie Houel, and Radhika Nagpal. 2020. “Eciton robotica: Design and Algorithms for an Adaptive Self-Assembling Soft Robot Collective.” Intl. Conferenc on Robotics and Automation (ICRA). icra2020-malley.pdf
Daniel Calovi, Paul Bardunias, Nicole Carey, J. Scott Turner, Radhika Nagpal, and Justin Werfel. 2019. “Surface curvature guides early construction activity in mound-building termites.” Philosophical Transactions of the Royal Society , 374, 1774. Publisher's VersionAbstract

Termite colonies construct towering, complex mounds, in a classic example of distributed agents coordinating their activity via interaction with a shared environment. The traditional explanation for how this coordination occurs focuses on the idea of a ‘cement pheromone’, a chemical signal left with deposited soil that triggers further deposition. Recent research has called this idea into question, pointing to a more complicated behavioural response to cues perceived with multiple senses. In this work, we explored the role of topological cues in affecting early construction activity in Macrotermes. We created artificial surfaces with a known range of curvatures, coated them with nest soil, placed groups of major workers on them and evaluated soil displacement as a function of location at the end of 1 h. Each point on the surface has a given curvature, inclination and absolute height; to disambiguate these factors, we conducted experiments with the surface in different orientations. Soil displacement activity is consistently correlated with surface curvature, and not with inclination nor height. Early exploration activity is also correlated with curvature, to a lesser degree. Topographical cues provide a long-term physical memory of building activity in a manner that ephemeral pheromone labelling cannot. Elucidating the roles of these and other cues for group coordination may help provide organizing principles for swarm robotics and other artificial systems.

This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.

Helen McCreery, Jenna Bilek, Radhika Nagpal, and Michael Breed. 2019. “Effects of load mass and size on cooperative transport in ants over multiple transport challenges.” Journal of Experimental Biology, doi: 10.1242/jeb.206821. Publisher's Version (open access)Abstract
Some ant species cooperatively transport a wide range of extremely large, heavy food objects of various shapes and materials. While previous studies have examined how object mass and size affect the recruitment of additional workers, less is understood about how these attributes affect the rest of the transport process. Using artificial baits with independently varying mass and size, we reveal their effects on cooperative transport in Paratrechina longicornis across two transport challenges: movement initiation and obstacle navigation. As expected, object mass was tightly correlated with number of porters as workers adjust group size to the task. Mass affected performance similarly across the two challenges, with groups carrying heavy objects having lower performance. Yet, object size had differing effects depending on the challenge. While larger objects led to reduced performance during movement initiation – groups took longer to start moving these objects and had lower velocities – there was no evidence for this during obstacle navigation, and the opposite pattern was weakly supported. If a group struggles to start moving an object, it does not necessarily predict difficulty navigating around obstacles; groups should persist in trying to move ‘difficult’ objects, which may be easier to transport later in the process. Additionally, groups hitting obstacles were not substantially disrupted, and started moving again sooner than at the start, despite the nest direction being blocked. Paratrechina longicornis transport groups never failed, performing well at both challenges while carrying widely varying objects, and even transported a bait weighing 1900 times the mass of an individual.
Mihai Duduta, Florian Berlinger, Radhika Nagpal, David Clarke, Robert Wood, and Zeynep Temel. 2019. “Electrically-latched compliant jumping mechanism based on a dielectric elastomer actuator.” Smart Materials and Structures , 28, 09LT01. duduta_sms_letter_2019.pdf
Lucie Houel. 2019. “Self-assembly of soft-robots in simulation inspired by army ant bridge behavior.” EPFL Master's Thesis.
Katherine Binney. 2019. “Teach a Fish to Swim: Evaluating the Ability of Turing Learning to Infer Schooling Behavior.” Senior Thesis, Harvard University.Abstract

Turing Learning is a promising evolutionary design method for swarm robotics that uses ob- servation of natural or artificial systems to infer controllers for agents in a swarm. However, Tur- ing Learning has thus far only been used to infer very simple swarm behaviors. In this work, we expand Turing Learning to infer dispersion, a much more complex swarm behavior, by a simulated school of robotic fish. Turing Learning depends on the co-evolution of replicas and classifiers. Replicas mimic ideal behavior and classifiers distinguish between data samples from replica and ideal agents. We model replicas and classifiers with neural networks and investigate the architecture of each component independently in order to determine needed modifications to Turing Learning for it to infer fish schooling. We find that previously formulated data samples led to the inference of behaviors that locally mimicked the agent trajectories in dispersion, yet poorly mimicked dispersion of an entire swarm. We present three alternative data samples that consider the spatial arrangement of agents in a swarm. We also introduce three new classifier fitness func- tions that accelerate evolution of high-accuracy classifiers. We find in a preliminary trial that using one of our data samples (metrics) and classifier fitness functions (foutputs) enables the successful inference of dispersion via Turing Learning.

Katherina Soltan, Jamie O'Brien, Florian Berlinger, Radhika Nagpal, and Jeff Dusek. 2018. “A Biomimetic Actuation Method for a Miniature, Low-Cost Multi-jointed Robotic Fish.” In IEEE/MTS OCEANS. oceans2018soltan.pdf
Florian Berlinger, Mihai Duduta, Hudson Gloria, David Clarke, Radhika Nagpal, and Robert Wood. 2018. “A Modular Dielectric Elastomer Actuator to Drive Miniature Autonomous Underwater Vehicles.” In Intl. Conf. on Robotics and Automation (ICRA).Abstract

Abstract—In this paper we present the design of a fin-like dielectric elastomer actuator (DEA) that drives a miniature autonomous underwater vehicle (AUV). The fin-like actuator is modular and independent of the body of the AUV. All electronics required to run the actuator are inside the 100 mm long 3D-printed body, allowing for autonomous mobility of the AUV. The DEA is easy to manufacture, requires no pre-stretch of the elastomers, and is completely sealed for underwater operation. The output thrust force can be tuned by stacking multiple actuation layers and modifying the Young’s modulus of the elastomers. The AUV is reconfigurable by a shift of its center of mass, such that both planar and vertical swimming can be demonstrated on a single vehicle. For the DEA we measured thrust force and swimming speed for various actuator designs ran at frequencies from 1Hz to 5Hz. For the AUV we demonstrated autonomous planar swimming and closed- loop vertical diving. The actuators capable of outputting the highest thrust forces can power the AUV to swim at speeds of up to 0.55body lengths per second. The speed falls in the upper range of untethered swimming robots powered by soft actuators. Our tunable DEAs also demonstrate the potential to mimic the undulatory motions of fish fins. 

(finalist for Best Conference Paper and Best Student Paper Awards)
Julia Ebert, Melvin Gauci, and Radhika Nagpal. 2018. “Multi-Feature Collective Decision Making in Robot Swarms.” In Intl. Conf. on Autonomous Agents and Multiagent Systems (AAMAS).Abstract

Collective decision making has been studied extensively in the fields of multi-agent systems and swarm robotics, inspired by its pervasiveness in biological systems such as honeybee and ant colonies. However, most previous research has focused on collective decision making on a single feature. In this work, we introduce and investigate the multi-feature collective decision making problem, where a collective must decide on multiple binary features simultaneously, given no a priori information about their relative difficulties. Each agent may only estimate one feature at any given time, but the agents can locally communicate their noisy estimates to arrive at a decision. We demonstrate a decentralized algorithm for single-feature decision making and a dynamic task allocation strategy that allows the agents to lock in decisions on multiple features in finite time. We validate our approach using simulated and physical Kilobot robots. Our results show that a collective can correctly classify a multi-feature environment, even if presented with pathological initial agent-to-feature allocations. 

Melinda Malley, Michael Rubenstein, and Radhika Nagpal. 2017. “Flippy: A Soft, Autonomous Climber with Simple Sensing and Control.” In IEEE/RSJ Intl Conference on Intelligent Robots and Systems (IROS).Abstract

Climbing robots have many potential applications including maintenance, monitoring, search and rescue, and self-assembly. While numerous climbing designs have been investigated, most are limited to stiff components. Flippy (Fig. 1) is a small, flipping biped robot with a soft, flexible body and on-board power and control. Due to its built-in compliance, flipping gait, and corkscrew gripper, it can autonomously climb up and down surfaces held at any angle relative to gravity and transition from one surface to another, without complex sensing or control. In this paper, we demonstrate the robot’s ability to flip consistently over a flat Velcro surface and 2D Velcro track, where it reliably climbs vertically, upside down and back to a flat surface, completing all the interior transitions in-between. 

Florian Berlinger, Jeff Dusek, Melvin Gauci, and Radhika Nagpal. 2017. “Robust Maneuverability of a Miniature Low-Cost Underwater Robot using Multiple Fin Actuation.” IEEE Robotics and Automation Letters (RA-L), PP, 99.Abstract

In this paper we present the design of a miniature (100 mm) autonomous underwater robot that is low-cost ($ 100), easy to manufacture, and highly maneuverable. A key aspect of the robot design that makes this possible is the use of low-cost magnet-in-coil actuators, which have a small profile and minimal sealing requirements. This allows us to create a robot with multiple flapping fin propulsors that independently control robot motions in surge, heave, and yaw. We present several results on the robot, including: (i) quantified open-loop swimming characteristics; (ii) autonomous behaviors using a pressure sensor and an IMU to achieve controlled swimming of prescribed trajectories; (iii) feedback from an optic sensor to enable homing to a light source. The robot is designed to form the basis for underwater swarm robotics testbeds, where low cost and ease of manufacture are critical, and 3D maneuverability allows testing complex coordination inspired by natural fish schools. Individually, miniature and low-cost underwater robots can also provide a platform for the study of 3D autonomy and marine vehicle dynamics in educational and resource-constrained settings. 

Kirstin Petersen and Radhika Nagpal. 2017. “Complex Design by Simple Robots: A Collective Embodied Intelligence Approach to Construction.” Architectural Design, Special Issue: Autonomous Assembly: Designing for a New Era of Collective Construction, 87, 4, Pp. 44-49.Abstract
Nature's builders – from termites to beavers – offer a model of collective intelligence that can inspire robotic construction. Kirstin Petersen, Assistant Professor in Electrical and Computer Engineering at Cornell University, Ithaca, New York, and Radhika Nagpal, Professor in Computer Science at the Harvard School of Engineering and Applied Sciences, Cambridge, Massachusetts, describe several recent projects in this field that they have been involved in, both separately and collaboratively.
Tsvetomira (Mira) Radeva, Anna Dornhaus, Nancy Lynch, Radhika Nagpal, and Hsin-Hao Su. 2017. “Costs of task allocation with local feedback: Effects of colony size and extra workers in social insects and other multi-agent systems.” PLoS computational biology, 13, 12. Publisher's Version (open-access)Abstract
(Author summary) 

Many complex systems have to allocate their units to different functions: cells in an embryo develop into different tissues, servers in a computer cluster perform different cal- culations, and insect workers choose particular tasks, such as brood care or foraging. Here we demonstrate that this process does not automatically become easier or harder with sys- tem size. If more workers are present than needed to complete the work available, some workers will always be idle; despite this, having surplus workers makes redistributing them across the tasks that need work much faster. Thus, unexpectedly, such surplus, idle workers may potentially significantly improve system performance. Our work suggests that interdisciplinary studies between biology and distributed computing can yield novel insights for both fields. 

Melvin Gauci, Monica Ortiz, Michael Rubenstein, and Radhika Nagpal. 2017. “Error Cascades in Collective Behavior: A Case Study of the Gradient Algorithm on 1000 Physical Agents.” In 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS).Abstract

The gradient, or hop count, algorithm is inspired by nat- ural phenomena such as the morphogen gradients present in multi-cellular development. It has several applications in multi-agent systems and sensor networks, serving as a basis for self-organized coordinate system formation, and finding shortest paths for message passing. It is a simple and well- understood algorithm in theory. However, we show here that in practice, it is highly sensitive to specific rare errors that emerge at larger scales. We implement it on a system of 1000 physical agents (Kilobot robots) that communicate asynchronously via a noisy wireless channel. We observe that spontaneous, short-lasting rare errors made by a sin- gle agent (e.g. due to message corruption) propagate spa- tially and temporally, causing cascades that severely hinder the algorithm’s functionality. We develop a mathematical model for temporal error propagation and validate it with experiments on 100 agents. This work shows how multi- agent algorithms that are believed to be simple and robust from theoretical insight may be highly challenging to im- plement on physical systems. Systematically understanding and quantifying their current limitations is a first step in the direction of improving their robustness for implementation.

Ben Green, Paul Bardunias, J. Scott Turner, Radhika Nagpal, and Justin Werfel. 2017. “Excavation and aggregation as organizing factors in de novo construction by mound-building termites.” Proceedings of the Royal Society B, 284, 1856. Publisher's VersionAbstract
Termites construct complex mounds that are orders of magnitude larger than any individual and fulfil a variety of functional roles. Yet the processes through which these mounds are built, and by which the insects organize their efforts, remain poorly understood. The traditional understanding focuses on stigmergy, a form of indirect communication in which actions that change the environment provide cues that influence future work. Termite construction has long been thought to be organized via a putative ‘cement pheromone’: a chemical added to deposited soil that stimulates further deposition in the same area, thus creating a positive feedback loop whereby coherent structures are built up. To investigate the detailed mechanisms and behaviours through which termites self-organize the early stages of mound construction, we tracked the motion and behaviour of major workers from two Macrotermes species in experimental arenas. Rather than a construction process focused on accumulation of depositions, as modelsbased on cement pheromone would suggest, our results indicated that the primary organizing mechanisms were based on excavation. Digging activity was focused on a small number of excavation sites, which in turn provided templates for soil deposition. This behaviour was mediated by a mechanism of aggregation, with termites being more likely to join in the work at an excavation site as the number of termites presently working at that site increased. Statistical analyses showed that this aggregation mechanism was a response to active digging, distinct from and unrelated to putative chemical cues that stimulate deposition. Agent-based simulations quantitatively supported the interpretation that the early stage of de novo construction is primarily organized by excavation and aggregation activity rather than by stigmergic deposition.
Nicole Carey, Radhika Nagpal, and Justin Werfel. 2017. “Fast, accurate, small-scale 3D scene capture using a low-cost depth sensor.” In IEEE Winter Conference on Applications of Computer Vision (WACV).Abstract

Commercially available depth sensing devices are pri- marily designed for domains that are either macroscopic, or static. We develop a solution for fast microscale 3D re- construction, using off-the-shelf components. By the addi- tion of lenses, precise calibration of camera internals and positioning, and development of bespoke software, we turn an infrared depth sensor designed for human-scale motion and object detection into a device with mm-level accuracy capable of recording at up to 30Hz. 

Elizabeth E. Esterley, Helen McCreery, and Radhika Nagpal. 2017. “Models of Adaptive Navigation, Inspired by Ant Cooperative Transport in the Presence of Obstacles.” In IEEE Artificial Life Conference (ALIFE).Abstract

Cooperative transport is an impressive example of collective behavior in ants, where groups of ants work together to move heavy food objects back to their nest over heterogeneous terrain. This behavior also serves as a model for bio-inspired robotics. While many studies have considered the mechanisms by which ants transport objects in simple settings, few have looked at how they deal with obstacles and heterogeneous terrain. A recent study on Paratrechina longicornis (crazy ants) demonstrated that groups of these ants implement a stochastic, adaptive, and robust cooperative transport strategy that allows them to succeed at navigating challenging obstacles that require moving away from their goal. In this paper, we use group-level computational models to investigate the significance and implications of this biological strategy. We develop an algorithm that reproduces important elements of the strategy, and compare it to several benchmark algorithms for a range of obstacle sizes and shapes. Our results show that, for smaller obstacles, the ant-inspired adaptive stochastic strategy is adept at efficient navigation, due to its ability to match the level of randomness it uses to unknown object size and shape. We also find some unexpected differences between our results and the original ant transport behavior, suggesting new biological experiments. 

(Best Student Paper, special commendation)
Serena Booth, James Tompkins, Hanspeter Pfister, Jim Waldo, Krzysztof Gajos, and Radhika Nagpal. 2017. “Piggybacking Robots: Human-Robot Overtrust in University Dormitory Security.” In ACM/IEEE International Conference on Human-Robot Interaction (HRI). (pdf)Abstract

Can overtrust in robots compromise physical security? We posi- tioned a robot outside a secure-access student dormitory and made it ask passersby for access. Individual participants were as likely to assist the robot in exiting the dormitory (40% assistance rate, 4/10 individuals) as in entering (19%, 3/16 individuals). Groups of people were more likely than individuals to assist the robot in entering (71%, 10/14 groups). When the robot was disguised as a food delivery agent for the ctional start-up Robot Grub, individ- uals were more likely to assist the robot in entering (76%, 16/21 individuals). Lastly, participants who identied the robot as a bomb threat demonstrated a trend toward assisting the robot (87%, 7/8 individuals, 6/7 groups). us, overtrust—the unfounded belief that the robot does not intend to deceive or carry risk—can represent a signicant threat to physical security at a university dormitory. 

(based on senior thesis, awarded Harvard Hoopes Prize)
Helen F. McCreery, Zachary A. Dix, Michael D. Breed, and Radhika Nagpal. 2016. “Collective strategy for obstacle navigation during cooperative transport by ants.” Journal of Experimental Biology, 219, 21, Pp. 3366-3375. Publisher's VersionAbstract

Group cohesion and consensus have primarily been studied in the context of discrete decisions, but some group tasks require making serial decisions that build on one another. We examine such collective problem solving by studying obstacle navigation during cooperative transport in ants. In cooperative transport, ants work together to move a large object back to their nest. We blocked cooperative transport groups of Paratrechina longicornis with obstacles of varying complexity, analyzing groups' trajectories to infer what kind of strategy the ants employed. Simple strategies require little information, but more challenging, robust strategies succeed with a wider range of obstacles. We found that transport groups use a stochastic strategy that leads to efficient navigation around simple obstacles, and still succeeds at difficult obstacles. While groups navigating obstacles preferentially move directly toward the nest, they change their behavior over time; the longer the ants are obstructed, the more likely they are to move away from the nest. This increases the chance of finding a path around the obstacle. Groups rapidly changed directions and rarely stalled during navigation, indicating that these ants maintain consensus even when the nest direction is blocked. Although some decisions were aided by the arrival of new ants, at many key points, direction changes were initiated within the group, with no apparent external cause. This ant species is highly effective at navigating complex environments, and implements a flexible strategy that works for both simple and more complex obstacles.