7 papers
Slot-MPC: Goal-Conditioned Model Predictive Control with Object-Centric Representations
Jonathan Spieler, Angel Villar-Corrales, Sven Behnke
Predictive world models enable agents to model scene dynamics and reason about the consequences of their actions. Inspired by human perception, object-centric world models capture…
VideoPCDNet: Video Parsing and Prediction with Phase Correlation Networks
Noel José Rodrigues Vicente, Enrique Lehner, Angel Villar-Corrales +2
Understanding and predicting video content is essential for planning and reasoning in dynamic environments. Despite advancements, unsupervised learning of object representations an…
TextOCVP: Object-Centric Video Prediction with Language Guidance
Angel Villar-Corrales, Gjergj Plepi, Sven Behnke
Understanding and forecasting future scene states is critical for autonomous agents to plan and act effectively in complex environments. Object-centric models, with structured late…
PlaySlot: Learning Inverse Latent Dynamics for Controllable Object-Centric Video Prediction and Planning
Angel Villar-Corrales, Sven Behnke
Predicting future scene representations is a crucial task for enabling robots to understand and interact with the environment. However, most existing methods rely on videos and sim…
SOLD: Slot Object-Centric Latent Dynamics Models for Relational Manipulation Learning from Pixels
Malte Mosbach, Jan Niklas Ewertz, Angel Villar-Corrales +1
Learning a latent dynamics model provides a task-agnostic representation of an agent's understanding of its environment. Leveraging this knowledge for model-based reinforcement lea…
MCDS-VSS: Moving Camera Dynamic Scene Video Semantic Segmentation by Filtering with Self-Supervised Geometry and Motion
Angel Villar-Corrales, Moritz Austermann, Sven Behnke
Autonomous systems, such as self-driving cars, rely on reliable semantic environment perception for decision making. Despite great advances in video semantic segmentation, existing…