18 citations · 24 across the 9 of their papers we have counts for
9 papers
Align and Filter: Improving Performance in Asynchronous On-Policy RL
Homayoun Honari, Roger Creus Castanyer, Michael Przystupa +3
Distributed training and increasing the gradient update frequency are practical strategies to accelerate learning and improve performance, but both exacerbate a central challenge:…
Point and Go: Intuitive Reference Frame Reallocation in Mode Switching for Assistive Robotics
A. Wang, C. Jiang, M. Przystupa +2
Operating high degree of freedom robots can be difficult for users of wheelchair mounted robotic manipulators. Mode switching in Cartesian space has several drawbacks such as unint…
Efficient Morphology-Aware Policy Transfer to New Embodiments
Michael Przystupa, Hongyao Tang, Martin Jagersand +4
Morphology-aware policy learning is a means of enhancing policy sample efficiency by aggregating data from multiple agents. These types of policies have previously been shown to he…
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…
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…
Analyzing Neural Jacobian Methods in Applications of Visual Servoing and Kinematic Control
Michael Przystupa, Masood Dehghan, Martin Jagersand +1
Designing adaptable control laws that can transfer between different robots is a challenge because of kinematic and dynamic differences, as well as in scenarios where external sens…
Sentence-Level BERT and Multi-Task Learning of Age and Gender in Social Media
Muhammad Abdul-Mageed, Chiyu Zhang, Arun Rajendran +3
Social media currently provide a window on our lives, making it possible to learn how people from different places, with different backgrounds, ages, and genders use language. In t…
Iroko: A Framework to Prototype Reinforcement Learning for Data Center Traffic Control
Fabian Ruffy, Michael Przystupa, Ivan Beschastnikh
Recent networking research has identified that data-driven congestion control (CC) can be more efficient than traditional CC in TCP. Deep reinforcement learning (RL), in particular…