VLDB 2026 Research / reviewers in the wild / expert
Keegan Wade
dblp:64/973
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2003
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 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 |
Reinforcement learning · 30% Robot manipulation · 30% Motion planning and robot control · 30% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Reinforcement learning
imitation learning |
0.0 | 1 | 2003 | Enabling real-time full-body imitation: a natural way of m-ansferring human movement to humanoids · ICRA 2003 |
Robotics › Motion planning and robot control › robot control
inverse kinematics |
0.0 | 1 | 2003 | Enabling real-time full-body imitation: a natural way of m-ansferring human movement to humanoids · ICRA 2003 |
Robotics › Robot manipulation › learning from demonstration
whole-body motion imitation |
0.0 | 1 | 2003 | Enabling real-time full-body imitation: a natural way of m-ansferring human movement to humanoids · ICRA 2003 |
Robotics › Legged, aerial and field robots › legged robots
humanoid locomotion |
0.0 | 1 | 2003 | Enabling real-time full-body imitation: a natural way of m-ansferring human movement to humanoids · ICRA 2003 |
Methods — techniques the papers use, named apart from their topics
inverse kinematics · 0.03d vision · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2003 | Enabling real-time full-body imitation: a natural way of m-ansferring human movement to humanoidsabstractWe seek intuitive, efficient ways to create and direct human-like behaviors for humanoid robots. Here we present a method to enable humanoid robots to acquire movements by imitation. The robot uses 3D vision to perceive the movements of a human teacher, and then estimates the teacher's body postures using a fast full-body inverse kinematics method that incorporates a kinematic model of the teacher. This solution is then mapped to the robot and reproduced in real-time. The robustness of the method is tested on a 30-degree-of-freedom Sarcos humanoid robot located at ATR using 3D vision data from external cameras and from head-mounted cameras. Marcia Riley, Ales Ude, Keegan Wade, Christopher G. Atkeson |
ICRA | 3 |