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Daniel S. Drew

dblp:229/0597 · DBLP profile ↗
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4ranked-venue papers
1as first author
3since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 1 first-author · 3 since 2021Systems, architecture and hardware · 2 · 1 first-author · 2 since 2021Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Robot manipulation · 50% Multi-agent systems · 50%
Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 77% Interaction techniques and input · 23%
Computer networks
1 paper
Wireless sensing and localization · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Multi-agent systems › multi-robot systems
distributed robotic teams
0.512021
Acoustic Communication and Sensing for Inflatable Modular Soft Robots · ICRA 2021
Robotics › Robot manipulation
soft robotics
0.512021
Acoustic Communication and Sensing for Inflatable Modular Soft Robots · ICRA 2021
Wireless sensing and localization
acoustic sensing
0.512021
Acoustic Communication and Sensing for Inflatable Modular Soft Robots · ICRA 2021
Interaction techniques and input
gesture input
0.112020
User-defined Swarm Robot Control · CHI 2020

Methods — techniques the papers use, named apart from their topics

piezoelectric surface transducers · 1.0acoustic signal transmission · 1.0elicitation study · 0.4controlled user study · 0.4
YearPublicationVenuePosition
2025 Discovery and Deployment of Emergent Robot Swarm Behaviors via Representation Learning and Real2Sim2Real Transfer
Connor Mattson, Varun Raveendra, Ricardo Vega, Cameron Nowzari, Daniel S. Drew, Daniel S. Brown
AAMAS5
2024 Empirical Study of Ground Proximity Effects for Small-scale Electroaerodynamic Thrusters
abstract
Electroaerodynamic (EAD) propulsion, where thrust is produced by collisions between electrostatically-accelerated ions and neutral air, is a potentially transformative method for indoor flight owing to its silent and solid-state nature. Like rotors, EAD thrusters exhibit changes in performance based on proximity to surfaces. Unlike rotors, they have no fragile and quickly spinning parts that have to avoid those surfaces; taking advantage of the efficiency benefits from proximity effects may be a route towards longer-duration indoor operation of ion-propelled fliers. This work presents the first empirical study of ground proximity effects for EAD propulsors, both individually and as quad-thruster arrays. It focuses on multi-stage ducted centimeter-scale actuators suitable for use on small robots envisioned for deployment in human-proximal and indoor environments. Three specific effects (ground, suckdown, and fountain lift), each occurring with a different magnitude at a different spacing from the ground plane, are investigated and shown to have strong dependencies on geometric parameters including thruster-to-thruster spacing, thruster protrusion from the fuselage, and inclusion of flanges or strakes. Peak thrust enhancement ranging from 300 to 600% is found for certain configurations operated in close proximity (0.2 mm) to the ground plane and as much as a 20% increase is measured even when operated centimeters away.
Grant Nations, C. Luke Nelson, Daniel S. Drew
ICRA3
2021 Acoustic Communication and Sensing for Inflatable Modular Soft Robots
abstract
Modular soft robots combine the strengths of two traditionally separate areas of robotics. As modular robots, they can show robustness to individual failure and reconfigurability; as soft robots, they can deform and undergo large shape changes in order to adapt to their environment, and have inherent human safety. However, for sensing and communication these robots also combine the challenges of both: they require solutions that are scalable (low cost and complexity) and efficient (low power) to enable collectives of large numbers of robots, and these solutions must also be able to interface with the high extension ratio elastic bodies of soft robots. In this work, we seek to address these challenges using acoustic signals produced by piezoelectric surface transducers that are cheap, simple, and low power, and that not only integrate with but also leverage the elastic robot skins for signal transmission. Importantly, to further increase scalability, the transducers exhibit multi-functionality made possible by a relatively flat frequency response across the audible and ultrasonic ranges. With minimal hardware, they enable directional contact-based communication, audible-range communication at a distance, and exteroceptive sensing. We demonstrate a subset of the decentralized collective behaviors that these functions make possible with multi-robot hardware implementations. The use of acoustic waves in this domain is shown to provide distinct advantages over existing solutions.
Daniel S. Drew, Matthew R. Devlin, Elliot Wright Hawkes, Sean Follmer
ICRA1
2020 User-defined Swarm Robot Control
abstract
A swarm of robots can accomplish more than the sum of its parts, and swarm systems will soon see increased use in applications ranging from tangible interfaces to search and rescue teams. However, effective human control of robot swarms has been shown to be demonstrably more difficult than controlling a single robot, and swarm-specific interactions methodologies are relatively underexplored. As we envision even non-expert users will have more daily in-person encounters with different numbers of robots in the future, we present a user-defined set of control interactions for tabletop swarm robots derived from an elicitation study. We investigated the effects of number of robots and proximity on the user's interaction and found significant effects. For instance, participants varied between using 1-2 fingers, one hand, and both hands depending on the group size. We also provide general design guidelines such as preferred interaction modality, common strategies, and a high-agreement interaction set.
Lawrence H. Kim, Daniel S. Drew, Veronika Domova, Sean Follmer
CHI2