Autonomous Robotic Manipulation
Modeling Top-Down Saliency for Visual Object Search
Interactive Perception
State Estimation and Sensor Fusion for the Control of Legged Robots
Probabilistic Object and Manipulator Tracking
Global Object Shape Reconstruction by Fusing Visual and Tactile Data
Robot Arm Pose Estimation as a Learning Problem
Learning to Grasp from Big Data
Gaussian Filtering as Variational Inference
Template-Based Learning of Model Free Grasping
Associative Skill Memories
Real-Time Perception meets Reactive Motion Generation
Autonomous Robotic Manipulation
Learning Coupling Terms of Movement Primitives
State Estimation and Sensor Fusion for the Control of Legged Robots
Inverse Optimal Control
Motion Optimization
Optimal Control for Legged Robots
Movement Representation for Reactive Behavior
Associative Skill Memories
Real-Time Perception meets Reactive Motion Generation
Visual-Inertial State Estimation for Legged Robots
] in two outdoor experiments. ©~IEEE. Reprinted, with permission, from []. ], we fuse high-rate pose (3D position and orientation) estimates of visual-inertial odometry (VIO, []) with velocity estimates from foot contacts and leg kinematics of a quadruped robot (Solo12). The kinematics allows for measuring the height above the ground, while the visual-inertial odometry complementarily estimates lateral motion and yaw rotation with respect to a local map.
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