Dexterity measures for the design and control of kinematically redundant manipulators
International Journal of Robotics Research
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Mathematical Introduction to Robotic Manipulation
A Mathematical Introduction to Robotic Manipulation
Physics-based Animation (Graphics Series)
Physics-based Animation (Graphics Series)
Physically based grasping control from example
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
Interaction capture and synthesis
ACM SIGGRAPH 2006 Papers
SIMBICON: simple biped locomotion control
ACM SIGGRAPH 2007 papers
Dextrous manipulation from a grasping pose
ACM SIGGRAPH 2009 papers
Optimizing walking controllers
ACM SIGGRAPH Asia 2009 papers
Robust task-based control policies for physics-based characters
ACM SIGGRAPH Asia 2009 papers
Motion fields for interactive character locomotion
ACM SIGGRAPH Asia 2010 papers
Stable Proportional-Derivative Controllers
IEEE Computer Graphics and Applications
Synthesis of detailed hand manipulations using contact sampling
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Contact-invariant optimization for hand manipulation
EUROSCA'12 Proceedings of the 11th ACM SIGGRAPH / Eurographics conference on Computer Animation
Hi-index | 0.00 |
We present a method for one-handed, task-based manipulation of objects. Our approach uses a mid-level, multi-phase approach to organize the problem into three phases. This provides an appropriate control strategy for each phase and results in cyclic finger motions that, together, accomplish the task. The exact trajectory of the object is never specified since the goal is defined by the final orientation and position of the object. All motion is physically based and guided by a control policy that is learned through a series of offline simulations. We also discuss practical considerations for our learning method. Variations in the synthesized motions are possible by tuning a scalarized multi-objective optimization. We demonstrate our method with two manipulation tasks, discussing the performance and limitations. Additionally, we provide an analysis of the robustness of the low-level controllers used by our framework.