25 citations · 58 across the 8 of their papers we have counts for
8 papers
Visual-Inertial and Leg Odometry Fusion for Dynamic Locomotion
Victor Dhédin, Haolong Li, Shahram Khorshidi +8
Implementing dynamic locomotion behaviors on legged robots requires a high-quality state estimation module. Especially when the motion includes flight phases, state-of-the-art appr…
Learning Temporally Extended Skills in Continuous Domains as Symbolic Actions for Planning
Jan Achterhold, Markus Krimmel, Joerg Stueckler
Problems which require both long-horizon planning and continuous control capabilities pose significant challenges to existing reinforcement learning agents. In this paper we introd…
Observability Analysis of Visual-Inertial Odometry with Online Calibration of Velocity-Control Based Kinematic Motion Models
Haolong Li, Joerg Stueckler
In this paper, we analyze the observability of the visual-inertial odometry (VIO) using stereo cameras with a velocity-control based kinematic motion model. Previous work shows tha…
DiffSDFSim: Differentiable Rigid-Body Dynamics With Implicit Shapes
Michael Strecke, Joerg Stueckler
Differentiable physics is a powerful tool in computer vision and robotics for scene understanding and reasoning about interactions. Existing approaches have frequently been limited…
Tracking 6-DoF Object Motion from Events and Frames
Haolong Li, Joerg Stueckler
Event cameras are promising devices for lowlatency tracking and high-dynamic range imaging. In this paper,we propose a novel approach for 6 degree-of-freedom (6-DoF)object motion t…
Explore the Context: Optimal Data Collection for Context-Conditional Dynamics Models
Jan Achterhold, Joerg Stueckler
In this paper, we learn dynamics models for parametrized families of dynamical systems with varying properties. The dynamics models are formulated as stochastic processes condition…
Weakly Supervised Learning of Multi-Object 3D Scene Decompositions Using Deep Shape Priors
Cathrin Elich, Martin R. Oswald, Marc Pollefeys +1
Representing scenes at the granularity of objects is a prerequisite for scene understanding and decision making. We propose PriSMONet, a novel approach based on Prior Shape knowled…
Sample-efficient Cross-Entropy Method for Real-time Planning
Cristina Pinneri, Shambhuraj Sawant, Sebastian Blaes +4
Trajectory optimizers for model-based reinforcement learning, such as the Cross-Entropy Method (CEM), can yield compelling results even in high-dimensional control tasks and sparse…