Human grasp choice and robotic grasp analysis
Dextrous robot hands
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
A limited memory algorithm for bound constrained optimization
SIAM Journal on Scientific Computing
Practical parameterization of rotations using the exponential map
Journal of Graphics Tools
Synthesizing animations of human manipulation tasks
ACM SIGGRAPH 2004 Papers
Physically based grasping control from example
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
Construction and optimal search of interpolated motion graphs
ACM SIGGRAPH 2007 papers
Musculotendon simulation for hand animation
ACM SIGGRAPH 2008 papers
Optimization-based interactive motion synthesis
ACM Transactions on Graphics (TOG)
Dextrous manipulation from a grasping pose
ACM SIGGRAPH 2009 papers
Contact-aware nonlinear control of dynamic characters
ACM SIGGRAPH 2009 papers
Hand Posture Subspaces for Dexterous Robotic Grasping
International Journal of Robotics Research
Synthesis of interactive hand manipulation
Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Potential field guide for humanoid multicontacts acyclic motion planning
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Sampling-based finger gaits planning for multifingered robotic hand
Autonomous Robots
Synthesis of detailed hand manipulations using contact sampling
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Discovery of complex behaviors through contact-invariant optimization
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Animating human lower limbs using contact-invariant optimization
ACM Transactions on Graphics (TOG)
| Hi-index | 0.00 |
We present a method for automatic synthesis of dexterous hand movements, given only high-level goals specifying what should happen to the object being manipulated. Results are presented on a wide range of tasks including grasping and picking up objects, twirling them between the fingers, tossing and catching, drawing. This work is an extension of the recent contact-invariant optimization (CIO) method, which introduced auxiliary decision variables directly specifying when and where contacts should occur, and optimized these variables together with the movement trajectory. Our contribution here is extending the unique contact model used in CIO which was specific to locomotion tasks, as well as applying the extended method systematically to hand manipulation.