V-COLLIDE: accelerated collision detection for VRML
VRML '97 Proceedings of the second symposium on Virtual reality modeling language
Computational geometry in C (2nd ed.)
Computational geometry in C (2nd ed.)
Knotting/Unknotting Manipulation of Deformable Linear Objects
International Journal of Robotics Research
Robot trajectory optimization using approximate inference
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Path planning in 1000+ dimensions using a task-space Voronoi bias
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Spatial relationship preserving character motion adaptation
ACM SIGGRAPH 2010 papers
Representation for knot-tying tasks
IEEE Transactions on Robotics
Manipulation Planning for Deformable Linear Objects
IEEE Transactions on Robotics
Planning 3-D Collision-Free Dynamic Robotic Motion Through Iterative Reshaping
IEEE Transactions on Robotics
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Motion can be described in several alternative representations, including joint configuration or end-effector spaces, but also more complex topology-based representations that imply a change of Voronoi bias, metric or topology of the motion space. Certain types of robot interaction problems, e.g. wrapping around an object, can suitably be described by so-called writhe and interaction mesh representations. However, considering motion synthesis solely in a topology-based space is insufficient since it does not account for additional tasks and constraints in other representations. In this paper, we propose methods to combine and exploit different representations for synthesis and generalization of motion in dynamic environments. Our motion synthesis approach is formulated in the framework of optimal control as an approximate inference problem. This allows for consistent combination of multiple representations (e.g. across task, end-effector and joint space). Motion generalization to novel situations and kinematics is similarly performed by projecting motion from topology-based to joint configuration space. We demonstrate the benefit of our methods on problems where direct path finding in joint configuration space is extremely hard whereas local optimal control exploiting a representation with different topology can efficiently find optimal trajectories. In real-world demonstrations, we highlight the benefits of using topology-based representations for online motion generalization in dynamic environments.