1 citations · 1 across the 6 of their papers we have counts for
6 papers
6D Assembly Pose Estimation by Point Cloud Registration for Robot Manipulation
K. Samarawickrama, G. Sharma, A. Angleraud +1
The demands on robotic manipulation skills to perform challenging tasks have drastically increased in recent times. To perform these tasks with dexterity, robots require perception…
Instructing Hierarchical Tasks to Robots by Verbal Commands
P. Telkes, A. Angleraud, R. Pieters
Natural language is an effective tool for communication, as information can be expressed in different ways and at different levels of complexity. Verbal commands, utilized for inst…
Multi-label Annotation for Visual Multi-Task Learning Models
G. Sharma, A. Angleraud, R. Pieters
Deep learning requires large amounts of data, and a well-defined pipeline for labeling and augmentation. Current solutions support numerous computer vision tasks with dedicated ann…
Co-speech gestures for human-robot collaboration
A. Ekrekli, A. Angleraud, G. Sharma +1
Collaboration between human and robot requires effective modes of communication to assign robot tasks and coordinate activities. As communication can utilize different modalities,…
Seq2Seq Imitation Learning for Tactile Feedback-based Manipulation
Wenyan Yang, Alexandre Angleraud, Roel S. Pieters +2
Robot control for tactile feedback-based manipulation can be difficult due to the modeling of physical contacts, partial observability of the environment, and noise in perception a…
Monolithic vs. hybrid controller for multi-objective Sim-to-Real learning
Atakan Dag, Alexandre Angleraud, Wenyan Yang +4
Simulation to real (Sim-to-Real) is an attractive approach to construct controllers for robotic tasks that are easier to simulate than to analytically solve. Working Sim-to-Real so…