4 citations · 8 across the 10 of their papers we have counts for
11 papers
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…
Dynamic Decision Frequency with Continuous Options
Amirmohammad Karimi, Jun Jin, Jun Luo +3
In classic reinforcement learning algorithms, agents make decisions at discrete and fixed time intervals. The duration between decisions becomes a crucial hyperparameter, as settin…
Video Class Agnostic Segmentation with Contrastive Learning for Autonomous Driving
Mennatullah Siam, Alex Kendall, Martin Jagersand
Semantic segmentation in autonomous driving predominantly focuses on learning from large-scale data with a closed set of known classes without considering unknown objects. Motivate…
Video Class Agnostic Segmentation Benchmark for Autonomous Driving
Mennatullah Siam, Alex Kendall, Martin Jagersand
Semantic segmentation approaches are typically trained on large-scale data with a closed finite set of known classes without considering unknown objects. In certain safety-critical…
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…
4-DoF Tracking for Robot Fine Manipulation Tasks
Mennatullah Siam, Abhineet Singh, Camilo Perez +1
This paper presents two visual trackers from the different paradigms of learning and registration based tracking and evaluates their application in image based visual servoing. The…
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…