4 citations · 8 across the 10 of their papers we have counts for
7 papers · 1 filter
Learning State Conditioned Linear Mappings for Low-Dimensional Control of Robotic Manipulators
Michael Przystupa, Kerrick Johnstonbaugh, Zichen Zhang +4
Identifying an appropriate task space that simplifies control solutions is important for solving robotic manipulation problems. One approach to this problem is learning an appropri…
Investigating the Benefits of Nonlinear Action Maps in Data-Driven Teleoperation
Michael Przystupa, Gauthier Gidel, Matthew E. Taylor +3
As robots become more common for both able-bodied individuals and those living with a disability, it is increasingly important that lay people be able to drive multi-degree-of-free…
Interpreting Behaviors and Geometric Constraints as Knowledge Graphs for Robot Manipulation Control
Chen Jiang, Allie Wang, Martin Jagersand
In this paper, we investigate the feasibility of using knowledge graphs to interpret actions and behaviors for robot manipulation control. Equipped with an uncalibrated visual serv…
CLIPUNetr: Assisting Human-robot Interface for Uncalibrated Visual Servoing Control with CLIP-driven Referring Expression Segmentation
Chen Jiang, Yuchen Yang, Martin Jagersand
The classical human-robot interface in uncalibrated image-based visual servoing (UIBVS) relies on either human annotations or semantic segmentation with categorical labels. Both me…
Deep Probabilistic Movement Primitives with a Bayesian Aggregator
Michael Przystupa, Faezeh Haghverd, Martin Jagersand +1
Movement primitives are trainable parametric models that reproduce robotic movements starting from a limited set of demonstrations. Previous works proposed simple linear models tha…
Offline Learning of Counterfactual Predictions for Real-World Robotic Reinforcement Learning
Jun Jin, Daniel Graves, Cameron Haigh +2
We consider real-world reinforcement learning (RL) of robotic manipulation tasks that involve both visuomotor skills and contact-rich skills. We aim to train a policy that maps mul…
Incremental Learning for Robot Perception through HRI
Sepehr Valipour, Camilo Perez, Martin Jagersand
Scene understanding and object recognition is a difficult to achieve yet crucial skill for robots. Recently, Convolutional Neural Networks (CNN), have shown success in this task. H…