Dan Song

Affiliations:
  • ChyronHego Tracab, Stockholm, Sweden
  • KTH Royal Institute of Technology, Centre for Autonomous Systems and the Computer Vision and Active Perception Lab, Stockholm, Sweden (2008 - 2012)
  • University of Southern California, Los Angeles, CA, USA (PhD 2008)


According to our database1, Dan Song authored at least 10 papers between 2009 and 2015.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2015
Task-Based Robot Grasp Planning Using Probabilistic Inference.
IEEE Trans. Robotics, 2015

2013
Predicting human intention in visual observations of hand/object interactions.
Proceedings of the 2013 IEEE International Conference on Robotics and Automation, 2013

A probabilistic framework for task-oriented grasp stability assessment.
Proceedings of the 2013 IEEE International Conference on Robotics and Automation, 2013

2012
Task-Based Grasp Adaptation on a Humanoid Robot.
Proceedings of the 10th IFAC Symposium on Robot Control, SyRoCo 2012, Dubrovnik, Croatia, 2012

From object categories to grasp transfer using probabilistic reasoning.
Proceedings of the IEEE International Conference on Robotics and Automation, 2012

2011
Embodiment-specific representation of robot grasping using graphical models and latent-space discretization.
Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011

Multivariate discretization for Bayesian Network structure learning in robot grasping.
Proceedings of the IEEE International Conference on Robotics and Automation, 2011

2010
Learning task constraints for robot grasping using graphical models.
Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010

Task modeling in imitation learning using latent variable models.
Proceedings of the 10th IEEE-RAS International Conference on Humanoid Robots, 2010

2009
Towards Grasp-Oriented Visual Perception for Humanoid Robots.
Int. J. Humanoid Robotics, 2009


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