Demo Data from EDBT 2026 / reviewers in the wild / expert
Jefferson A. Coelho Jr.
dblp:99/4133
· DBLP profile ↗
5ranked-venue papers
5as first author
0since 2021 · last 2000
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 5 · 5 first-authorSystems, architecture and hardware · 3 · 3 first-author
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
5 papers |
Robot manipulation · 37% Reinforcement learning · 35% Motion planning and robot control · 28% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Parallel and multicore computing · 100% |
Topics — the 7 heaviest of 9, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation
grasping |
0.0 | 3 | 1996 | Online grasp synthesis · ICRA 1996 Control Pre-Imaging for Multifmgered Grasp Synthesis · ICRA 1994 Optimal Multifingered Grasp Synthesis · ICRA 1994 |
Robotics › Motion planning and robot control › robot control
motion control |
0.0 | 1 | 1995 | Parallel Optimization of Motion Controllers via Policy Iteration · NIPS 1995 |
Machine learning › Reinforcement learning › dynamic programming
policy iteration |
0.0 | 1 | 1995 | Parallel Optimization of Motion Controllers via Policy Iteration · NIPS 1995 |
Robotics › Robot manipulation › grasping › grasp planning
grasp synthesis |
0.0 | 1 | 1994 | Optimal Multifingered Grasp Synthesis · ICRA 1994 |
Machine learning › Reinforcement learning
non-stationary reinforcement learning |
0.0 | 1 | 2000 | Learning in Non-stationary Conditions: A Control Theoretic Approach · ICML 2000 |
Parallel and multicore computing › parallel computing
parallel optimization |
0.0 | 1 | 1995 | Parallel Optimization of Motion Controllers via Policy Iteration · NIPS 1995 |
Robotics › Motion planning and robot control › robot control
optimal control |
0.0 | 1 | 1994 | Optimal Multifingered Grasp Synthesis · ICRA 1994 |
Methods — techniques the papers use, named apart from their topics
policy iteration · 0.0parallel optimization · 0.0control theory · 0.0preimage of grasp controller · 0.0friction compensation · 0.0optimal control · 0.0meta-level control · 0.0control pre-imaging · 0.0control composition · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2000 | Learning in Non-stationary Conditions: A Control Theoretic Approach
Jefferson A. Coelho Jr., Roderic A. Grupen |
ICML | 1 |
| 1996 | Online grasp synthesisabstractThis paper addresses the online grasp synthesis problem, in which statically stable grasp configurations are to be generated given instantaneous contact information. We use the preimage of a given grasp controller to address object resolution, friction compensation, and resource allocation in a online fashion. The approach can be applied to piecewise smooth object geometries, and it can be extended to accommodate any number of contacts and 3D objects. Jefferson A. Coelho Jr., Roderic A. Grupen |
ICRA | 1 |
| 1995 | Parallel Optimization of Motion Controllers via Policy Iteration
Jefferson A. Coelho Jr., Ramesh K. Sitaraman, Roderic A. Grupen |
NIPS | 1 |
| 1994 | Optimal Multifingered Grasp SynthesisabstractThis paper discusses how grasp synthesis can be cast as a control composition problem. Two elemental grasp controllers are presented and composed into a general purpose grasp controller through the use of standard optimal control techniques. The resulting grasp synthesis procedure is scalable on the number of contacts, is not tied to any specific object representation scheme, and can incorporate kinematic constraints into its resolution.> Jefferson A. Coelho Jr., Roderic A. Grupen |
ICRA | 1 |
| 1994 | Control Pre-Imaging for Multifmgered Grasp SynthesisabstractThis paper discusses the issues involved in developing a grasp controller, within the framework of control composition, and introduces control pre-imaging. Pre-imaging is a design technique for augmenting the performance of a baseline controller through the opportune activation of metalevel control actions. Such metalevel control actions are activated whenever the current system state is associated to past unsuccessful patterns of interaction between the existing predictable, stable controller and the environment. The technique is illustrated by the implementation of a grasp controller capable of generating grasp configurations through successive, local refinements, given the position and normal of each contact.> Jefferson A. Coelho Jr., Roderic A. Grupen |
ICRA | 1 |