Demo Data from EDBT 2026 / reviewers in the wild / expert
Nishanth Mankame
dblp:382/6192
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021
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 |
Motion planning and robot control · 67% Legged, aerial and field robots · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control › robot control
compliant motion control |
0.9 | 1 | 2025 | AquaMILR: Mechanical Intelligence Simplifies Control of Undulatory Robots in Cluttered Fluid Environments · ICRA 2025 |
Robotics › Legged, aerial and field robots
limbless locomotion |
0.9 | 1 | 2025 | AquaMILR: Mechanical Intelligence Simplifies Control of Undulatory Robots in Cluttered Fluid Environments · ICRA 2025 |
Robotics › Motion planning and robot control
robot control |
0.9 | 1 | 2025 | AquaMILR: Mechanical Intelligence Simplifies Control of Undulatory Robots in Cluttered Fluid Environments · ICRA 2025 |
Methods — techniques the papers use, named apart from their topics
tension feedback control · 0.9open-loop control · 0.9cable-driven actuation · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | AquaMILR: Mechanical Intelligence Simplifies Control of Undulatory Robots in Cluttered Fluid EnvironmentsabstractWhile undulatory swimming of elongate limbless robots has been extensively studied in open hydrodynamic environments, less research has been focused on limbless locomotion in complex, cluttered aquatic environments. Motivated by the concept of mechanical intelligence [1], where controls for obstacle navigation can be offloaded to passive body mechanics in terrestrial limbless locomotion, we hypothesize that principles of mechanical intelligence can be extended to cluttered hydrodynamic regimes. To test this, we developed an untethered limbless robot capable of undulatory swimming on water surfaces, utilizing a bilateral cable-driven mechanism inspired by organismal muscle actuation morphology to achieve programmable anisotropic body compliance. We demonstrated through robophysical experiments that, similar to terrestrial locomotion, an appropriate level of body compliance can facilitate emergent swim through complex hydrodynamic environments under pure open-loop control. Moreover, we found that swimming performance depends on undulation frequency, with effective locomotion achieved only within a specific frequency range. This contrasts with highly damped terrestrial regimes, where inertial effects can often be neglected. Further, to enhance performance and address the challenges posed by nondeterministic obstacle distributions, we incorporated computational intelligence by developing a real-time body compliance tuning controller based on cable tension feedback. This controller improves the robot's robustness and overall speed in heterogeneous hydrodynamic environments. Tianyu Wang 0010, Nishanth Mankame, Matthew Fernandez, Velin Kojouharov, Daniel I. Goldman |
ICRA | 2 |