Search Results for author:
Found 15 papers, 3 papers with code
Physics-Based Rigid Body Object Tracking and Friction Filtering From RGB-D Videos
In this paper, we propose a novel approach for real-to-sim which tracks rigid objects in 3D from RGB-D images and infers physical properties of the objects.
Online Calibration of a Single-Track Ground Vehicle Dynamics Model by Tight Fusion with Visual-Inertial Odometry
Our method calibrates and adapts the dynamics model online and facilitates accurate forward prediction conditioned on future control inputs.
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-based Relational Object Matching Across Views
Our approach compares favorably to previous state-of-the-art object-level matching approaches and achieves improved performance over a pure keypoint-based approach for large view-point changes.
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.
Visual-Inertial Odometry with Online Calibration of Velocity-Control Based Kinematic Motion Models
Visual-inertial odometry (VIO) is an important technology for autonomous robots with power and payload constraints.
Tracking 6-DoF Object Motion from Events and Frames
Event cameras are promising devices for lowlatency tracking and high-dynamic range imaging.
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.
Weakly Supervised Learning of Multi-Object 3D Scene Decompositions Using Deep Shape Priors
Our approach learns to decompose images of synthetic scenes with multiple objects on a planar surface into its constituent scene objects and to infer their 3D properties from a single view.
Semi-Supervised Learning of Multi-Object 3D Scene Representations
By differentiable rendering, we train our model to decompose scenes self-supervised from RGB-D 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
Where Does It End? -- Reasoning About Hidden Surfaces by Object Intersection Constraints
To the best of our knowledge, our approach is the first method to incorporate such physical plausibility constraints on object intersections for shape completion of dynamic objects in an energy minimization framework.
DirectShape: Direct Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation
Scene understanding from images is a challenging problem encountered in autonomous driving.
