Coupled dynamical system based arm–hand grasping model for learning fast adaptation strategies
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The explicit coupling between arm and hand dynamics ensures that finger closure will be naturally triggered at the right time, even when the hand motion is delayed or accelerated to adapt to changes in the prediction of the catching point.
“The explicit coupling between arm and hand dynamics ensures that the finger closure will be naturally triggered at the right time, even when the hand motion is delayed or accelerated to adapt to changes in the prediction of the catching point.”
From Catching Objects in Flight (2014) · cites this paper as [38]
Section 4.3.2 reports that the coupled model coordinates finger motion with a redirected hand movement, closing on a falling object at the right time; Section 4.3.1 shows that explicit coupling prevents premature closure after a target switch. The paper does not establish the more specific scenario of hand motion being delayed or accelerated in response to changes in the predicted catching point. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe spatial coupling between hand and fingers ensures timely closure of the fingers while following the learned dynamics as closely as possible, even in the presence of arbitrary perturbations.
“Such a spatial coupling between hand and fingers ensures timely closure of the fingers while following the learned dynamics as closely as possible, even in the presence of arbitrary perturbations.”
From Catching Objects in Flight (2014) · cites this paper as [38]
Section 4.3.1 reports that explicit coupling delays finger closure after a target switch and produces a successful grasp; Section 4.2.2 says finger trajectories resume the unperturbed motion model after perturbation. The claim overextends these results to arbitrary perturbations: Section 5.1 notes that perturbations far from demonstrated motions may yield infeasible robot motions, and the experiments stayed within the center of the robot’s workspace. Page numbers are not available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredIn [38], the authors show that modeling arm and hand motion through coupled dynamical systems (CDS) is advantageous over classic approaches.
“In [38], we show that modeling arm and hand motion through coupled dynamical systems (CDS) is advantageous over the classic approaches reviewed previously.”
From Catching Objects in Flight (2014) · cites this paper as [38]
The Conclusion and discussion says that explicit coupling of hand and finger motion was advantageous over implicit coupling learned with other density-based methods. Section 4.3.1 illustrates that advantage against a single-GMM approach. The paper does not establish an advantage over classic approaches generally, as the citing sentence suggests. (LLM, conf=0.78)
Experimental — accuracy not yet measuredThe paper combines three strands of previous work: learning to predict trajectory of fast-moving objects [22], learning hand-finger coordinated motion [38], and controlling timing of motion with DSs [23].
“We combine three strands of our previous works on 1) learning how to predict accurately the trajectory of fast-moving objects [22], 2) learning hand-finger coordinated motion [38], and 3) controlling the timing of motion when controlled with DSs [23].”
From Catching Objects in Flight (2014) · cites this paper as [38]
The CDS approach offers robust and fast control of the complete hand-arm system without resorting to extensive parameter tuning.
“This offers a robust and fast control of the complete hand-arm system without resorting to extensive parameter tuning.”
From Catching Objects in Flight (2014) · cites this paper as [38]
The CDS model ensures coordinated motion of all joints by coupling reaching and grasping dynamical systems.
“We also encode the hand-finger coupling using the CDS model [38] to ensure the coordinated motion of all joints.”
From Catching Objects in Flight (2014) · cites this paper as [38]
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Research works printed but not linked: 32
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Self-citation
- Authors
- Shukla, A., & Billard, A.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Corresponding author
- Shukla, Ashwini
- ORCID
- Billard, Aude
- Venue
- Robotics and Autonomous Systems
- Publisher
- Elsevier BV
- Published
- 2012-03
- Volume
- 60
- Issue
- 3
- Pages
- 424-440
- ISSN
- 0921-8890 (ISSN-L)
- OpenAlex
- W2143172838
- Cited by
- 55
- References
- 0
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 4 · 2021: 3 · 2020: 4 · 2019: 5 · 2018: 7 · 2017: 2 · 2016: 2 · 2015: 4 · 2014: 8 · 2013: 3 · 2012: 8
- FWCI
- 9.68
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Robotics and Autonomous Systems (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- European Commission (248258)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- reach-to-grasp, programming by demonstration, coupled dynamical systems, arm-hand coordination, finger movement, replanning, human-robot interaction, iCub humanoid robot, hand kinematics
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