2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020
DOI: 10.1109/iros45743.2020.9341570
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Learning and Sequencing of Object-Centric Manipulation Skills for Industrial Tasks

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Cited by 22 publications
(37 citation statements)
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“…Manipulation skills on R 3 × S 3 : To test our approach in a robotic setting, we consider a re-orient skill from [27], which involves lifting a previously-grasped object, rotating the endeffector, and placing the object back on its original location with a modified orientation (see Fig. 5).…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Manipulation skills on R 3 × S 3 : To test our approach in a robotic setting, we consider a re-orient skill from [27], which involves lifting a previously-grasped object, rotating the endeffector, and placing the object back on its original location with a modified orientation (see Fig. 5).…”
Section: Methodsmentioning
confidence: 99%
“…where µ + is mean of the resulting Gaussian (that we are estimating) and Λ y,s = Γ µy,s→µ + (Σ −1 y,s ). We can rewrite (27) by defining the vector (µ + ) = Log µ + (µ y,1 ) T • • • Log µ + (µ y,S ) T T and the block diagonal matrix Λ = blockdiag(Λ y,1 , • • • , Λ y,S ). This results in J having the form of the objective function used to compute the empirical mean v of a Gaussian distribution on a Riemannian manifold M (Eq.…”
Section: B3 Blendingmentioning
confidence: 99%
“…Hidden Markov models (HMMs) have been typically used as a generative model for recognition and generation of movement skills in robotics (Asfour et al, 2008; Duque et al, 2019; Lee and Ott, 2010; Mor et al, 2021; Rozo et al, 2020; Tanwani and Calinon, 2017; Vakanski et al, 2012). For example, Kulic et al (2008) used HMMs to incrementally group whole-body motions based on their relative distance in HMM space.…”
Section: Related Workmentioning
confidence: 99%
“…Unit quaternions are a convenient way to represent an orientation since they are compact, not redundant, and prevent gimbal lock. Also, they provide strong stability guarantees in closed-loop orientation control [7], they have been recently exploited in robot skills learning [34], and for data-efficient robot control tuning [21] under a Riemannianmanifold formulation.…”
Section: B Orientation Encoding On Smentioning
confidence: 99%
“…The first set of experiments is based on a dataset collected while a human operator performs kinesthetic demonstrations of a grasping skill. This particular grasping motion includes a 90°rotation when approaching the object for performing a side grasp [34]. The demonstration trajectories start from different end-effector poses, and they reach the same target position with slightly different orientations.…”
Section: Real Grasping Taskmentioning
confidence: 99%