10 citations · 45 across the 8 of their papers we have counts for
8 papers
A Benchmark and a Baseline for Robust Multi-view Depth Estimation
Philipp Schröppel, Jan Bechtold, Artemij Amiranashvili +1
Recent deep learning approaches for multi-view depth estimation are employed either in a depth-from-video or a multi-view stereo setting. Despite different settings, these approach…
Pre-training of Deep RL Agents for Improved Learning under Domain Randomization
Artemij Amiranashvili, Max Argus, Lukas Hermann +2
Visual domain randomization in simulated environments is a widely used method to transfer policies trained in simulation to real robots. However, domain randomization and augmentat…
Scaling Imitation Learning in Minecraft
Artemij Amiranashvili, Nicolai Dorka, Wolfram Burgard +2
Imitation learning is a powerful family of techniques for learning sensorimotor coordination in immersive environments. We apply imitation learning to attain state-of-the-art perfo…
Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor Control
Lukas Hermann, Max Argus, Andreas Eitel +3
We propose Adaptive Curriculum Generation from Demonstrations (ACGD) for reinforcement learning in the presence of sparse rewards. Rather than designing shaped reward functions, AC…
CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity
Aditya Bhatt, Daniel Palenicek, Boris Belousov +4
Sample efficiency is a crucial problem in deep reinforcement learning. Recent algorithms, such as REDQ and DroQ, found a way to improve the sample efficiency by increasing the upda…
Motion Perception in Reinforcement Learning with Dynamic Objects
Artemij Amiranashvili, Alexey Dosovitskiy, Vladlen Koltun +1
In dynamic environments, learned controllers are supposed to take motion into account when selecting the action to be taken. However, in existing reinforcement learning works motio…
TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning
Artemij Amiranashvili, Alexey Dosovitskiy, Vladlen Koltun +1
Our understanding of reinforcement learning (RL) has been shaped by theoretical and empirical results that were obtained decades ago using tabular representations and linear functi…
Stochastic switching between multistable oscillation patterns of the Min-system
Artemij Amiranashvili, Nikolas D Schnellbächer, Ulrich S Schwarz
The spatiotemporal oscillation patterns of the proteins MinD and MinE are used by the bacterium E. coli to sense its own geometry. Strikingly, both computer simulations and experim…