Physics-empowered perception for robot grasping and dexterous manipulation

Zhang, Li
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Other Contributors
Trinkle, Jeffrey C.
Lyu, Siwei
Anderson, Kurt S.
Cutler, Barbara M.
Carothers, Christopher D.
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Computer science
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Full Citation
To address such weaknesses, we define the C-SLAM problem to set requirements on perception algorithms for the ultimate goal of human-like Bayesian grasping and manipulation. It requires simultaneous object localization and modeling of its physical properties during manipulating the object. In this thesis, we propose two estimation frameworks to attack the C-SLAM problem. Both frameworks explicitly adopt a dynamic system transition model to characterize real-time interactions between the robot end effector and the object at the center of respective stochastic system transition models. Their main difference is that one framework adopts a computationally complex full-blown dynamic formulation while the other significantly reduces the computational cost by decomposing it into sub-models of contact mode prediction and state propagation, which are originally tightly coupled in physics. Constraints are hence relaxed at certain sampled states. Both frameworks are proven effective in capturing dynamic behaviors from applications to various grasping scenarios and the relaxation model gives better computation performance.
December 2013
School of Science
Dept. of Computer Science
Rensselaer Polytechnic Institute, Troy, NY
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