Search Results for author:
Found 10 papers, 4 papers with code
Learning a Terrain- and Robot-Aware Dynamics Model for Autonomous Mobile Robot Navigation
In this article, we propose a novel approach for learning a probabilistic, terrain- and robot-aware forward dynamics model (TRADYN) which can adapt to such variations and demonstrate its use for navigation.
Attention Normalization Impacts Cardinality Generalization in Slot Attention
Object-centric scene decompositions are important representations for downstream tasks in fields such as computer vision and robotics.
Context-Conditional Navigation with a Learning-Based Terrain- and Robot-Aware Dynamics Model
Terrain properties such as friction coefficients may vary over time depending on the location of the robot.
Black-Box vs. Gray-Box: A Case Study on Learning Table Tennis Ball Trajectory Prediction with Spin and Impacts
Our gray-box approach builds on a physical model.
Learning Temporally Extended Skills in Continuous Domains as Symbolic Actions for Planning
In this paper we introduce a novel hierarchical reinforcement learning agent which links temporally extended skills for continuous control with a forward model in a symbolic discrete abstraction of the environment's state for planning.
Explore the Context: Optimal Data Collection for Context-Conditional Dynamics Models
In this paper, we learn dynamics models for parametrized families of dynamical systems with varying properties.
Learning to Identify Physical Parameters from Video Using Differentiable Physics
Video prediction models often learn a latent representation of video which is encoded from input frames and decoded back into images.
Sample-efficient Cross-Entropy Method for Real-time Planning
However, their sampling inefficiency prevents them from being used for real-time planning and control.
Model-based Reinforcement Learning
reinforcement-learning
+1
Planning from Images with Deep Latent Gaussian Process Dynamics
We propose to learn a deep latent Gaussian process dynamics (DLGPD) model that learns low-dimensional system dynamics from environment interactions with visual observations.
Variational Network Quantization
In this paper, the preparation of a neural network for pruning and few-bit quantization is formulated as a variational inference problem.
