6 citations · 8 across the 4 of their papers we have counts for
4 papers
Learning Robotic Manipulation Skills Using an Adaptive Force-Impedance Action Space
Maximilian Ulmer, Elie Aljalbout, Sascha Schwarz +1
Intelligent agents must be able to think fast and slow to perform elaborate manipulation tasks. Reinforcement Learning (RL) has led to many promising results on a range of challeng…
Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning
Elie Aljalbout, Maximilian Ulmer, Rudolph Triebel
Vision-based reinforcement learning (RL) is a promising approach to solve control tasks involving images as the main observation. State-of-the-art RL algorithms still struggle in t…
Making Curiosity Explicit in Vision-based RL
Elie Aljalbout, Maximilian Ulmer, Rudolph Triebel
Vision-based reinforcement learning (RL) is a promising technique to solve control tasks involving images as the main observation. State-of-the-art RL algorithms still struggle in…
Learning Vision-based Reactive Policies for Obstacle Avoidance
Elie Aljalbout, Ji Chen, Konstantin Ritt +2
In this paper, we address the problem of vision-based obstacle avoidance for robotic manipulators. This topic poses challenges for both perception and motion generation. While most…