60 citations
- McGill UniversityCA5 papers
- Nantes UniversitéFR4 papers
- Chinese Academy of SciencesCN3 papers
- Georgia Institute of TechnologyUS3 papers
- University of SfaxTN3 papers
- Institute of Cognitive and Brain SciencesUS2 papers
- Institute of Intelligent MachinesCN2 papers
- Johns Hopkins UniversityUS2 papers
- McGovern Institute for Brain ResearchUS2 papers
- Technological University DublinIE2 papers
- Boston CollegeUS1 paper
- Brain (Germany)DE1 paper
10 papers · 1 filter
Multi-Touch and Bending Sensing Using Electrical Impedance Tomography for Robotics
Haofeng Chen, Bedrich Himmel, Bin Li +2
Electrical Impedance Tomography (EIT) offers a promising solution for distributed tactile sensing with minimal wiring and full-surface coverage in robotic applications. However, EI…
Information-Theoretic Abstractions for Resource-Constrained Agents via Mixed-Integer Linear Programming
Daniel T. Larsson, Dipankar Maity, Panagiotis Tsiotras
In this paper, a mixed-integer linear programming formulation for the problem of obtaining task-relevant, multi-resolution, graph abstractions for resource-constrained agents is pr…
Input-Shaping for Feed-Forward Control of Cable-Driven Parallel Robots
Sana Baklouti, Eric Courteille, Philippe Lemoine +2
This paper deals with the use of input-shaping filters in conjunction with a feed-forward control of Cable-Driven Parallel Robots (CDPRs), while integrating cable tension calculati…
Online Center of Mass Estimation for a Humanoid Wheeled Inverted Pendulum Robot
Munzir Zafar, Akash Patel, Bogdan Vlahov +3
We present a novel application of robust control and online learning for the balancing of a n Degree of Freedom (DoF), Wheeled Inverted Pendulum (WIP) humanoid robot. Our technique…
Learning Generalizable Robot Skills from Demonstrations in Cluttered Environments
Muhammad Asif Rana, Mustafa Mukadam, Seyed Reza Ahmadzadeh +2
Learning from Demonstration (LfD) is a popular approach to endowing robots with skills without having to program them by hand. Typically, LfD relies on human demonstrations in clut…
Real-world Multi-object, Multi-grasp Detection
Fu-Jen Chu, Ruinian Xu, Patricio A. Vela
A deep learning architecture is proposed to predict graspable locations for robotic manipulation. It considers situations where no, one, or multiple object(s) are seen. By defining…