Technische Universität München Robotics and Embedded Systems

Caixia Cai


Research Assistant

Room MI 03.07.037
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Address Institut für Informatik VI
Technische Universität München
Boltzmannstraße 3
85748 Garching bei München

Caixia Cai

Curriculum Vitæ

Current Research Interests



1. Visual Servoing

* 6D Stereo Uncalibrated Image-Based Visual Servoing (IROS 2014)

This video shows an implementation of a 6D image-based visual servoing control. The extrinsic parameters of the camera and the kinematic/dynamic parameters of the robot are considered as unknown. The visual-servoing control uses error functions defined in a 3D visual space (measured in pixels). The control approach can be integrated seamless with other control strategies to obtain different dynamic behaviours. In order to overcome the limitation of the stereo-rig (visual occlusion), a new camera model is designed together with a robust control law. Now, in case of occlusion, the stereo-rig can be moved. The control scheme will adapt to the new camera parameters.The demo also shows different dynamic/kinematic behavior for the robot.

* 3D Stereo Uncalibrated Image-Based Visual Servoing (IROS 2013)

* Safe Human-Robot Interaction: Human Avoidance

2. Robotics

* Robot Kinematic and Dynamic Modeling

help the development of useful tools for robot modeling (Kinematic and Dynamic models)

* Human-Robot Interaction (Teaching & Execution)

During the teaching phase, the reasoning engine uses the information from the perception module to associate involved objects/actors with the task. The Process and Tasks are stored in an Ontology-based Knowledge Data Base. This knowledge can be used later in more complex Process. The Semantic abstraction allows the decoupling of the Problem Space from the Solution Space. Thereby, the problem description and analysis is isolated from the execution and scenario-specific parameters. This research is associated to the project SMErobotics .

* Human-Robot Collaboration

The architecture can be used for Human-Robot Collaboration. The semantic description allows to integrate Human tasks with Robot Tasks. In this way, the strengths of both actors can be used to optimize the process. This research is associated to the project SMErobotics .

3. Computer Vision

* Scene Perception and Recognition Using Point Clouds in Human-Robot Interaction Scenarios

For More Information please visit the Homepage : Dr. Emmanuel Carlos Dean Leon and my colleague Nikhil Somani