101 citations · 136 across the 20 of their papers we have counts for
6 papers · 1 filter
Benchmarks for Physical Reasoning AI
Andrew Melnik, Robin Schiewer, Moritz Lange +4
Physical reasoning is a crucial aspect in the development of general AI systems, given that human learning starts with interacting with the physical world before progressing to mor…
Behavioral Cloning via Search in Embedded Demonstration Dataset
Federico Malato, Florian Leopold, Ville Hautamaki +1
Behavioural cloning uses a dataset of demonstrations to learn a behavioural policy. To overcome various learning and policy adaptation problems, we propose to use latent space to i…
Contrastive Language, Action, and State Pre-training for Robot Learning
Krishan Rana, Andrew Melnik, Niko Sünderhauf
In this paper, we introduce a method for unifying language, action, and state information in a shared embedding space to facilitate a range of downstream tasks in robot learning. O…
Shape complexity estimation using VAE
Markus Rothgaenger, Andrew Melnik, Helge Ritter
In this paper, we compare methods for estimating the complexity of two-dimensional shapes and introduce a method that exploits reconstruction loss of Variational Autoencoders with…
Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition
Stephanie Milani, Anssi Kanervisto, Karolis Ramanauskas +27
To facilitate research in the direction of fine-tuning foundation models from human feedback, we held the MineRL BASALT Competition on Fine-Tuning from Human Feedback at NeurIPS 20…
Stroke-based Rendering: From Heuristics to Deep Learning
Florian Nolte, Andrew Melnik, Helge Ritter
In the last few years, artistic image-making with deep learning models has gained a considerable amount of traction. A large number of these models operate directly in the pixel sp…