Estimating the non-linear dynamics of free-flying objects
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In previous work [22], the authors learned from demonstrations the dynamics of complex objects whose shapes are arbitrary, nonrigid, or whose points of interest are not at the COM.
“In our previous work [22], we learned from demonstrations the dynamics of more complex objects whose shapes are arbitrary, nonrigid, or their points of interest are not at the COM.”
From Catching Objects in Flight (2014) · cites this paper as [22]
The Introduction describes learning motion dynamics from human throwing demonstrations, and Section 2.4 says the method handles arbitrarily shaped objects and points away from the center of mass. Section 4.1.1 also studies a half-filled bottle whose moment of inertia changes in flight, but our copy does not establish that the method was applied to nonrigid objects themselves. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe DS encoding provides an efficient way to model the dynamics of a moving object solely by observing examples of the object's motion in space.
“We encode the demonstrations using a dynamical system (DS) that provides an efficient way to model the dynamics of a moving object, solely by observing examples of the object's motion in space.”
From Catching Objects in Flight (2014) · cites this paper as [22]
The machine-learning approach avoids gathering a priori information on the object and estimates directly the nonlinear dynamics of the object's flight from observing several examples of such trajectories.
“The machine-learning approach we follow in this paper avoids gathering such a priori information on the object and estimates directly the nonlinear dynamics of the object's flight from observing several examples of such trajectories [22].”
From Catching Objects in Flight (2014) · cites this paper as [22]
The DS encoding does not require any prior information on the physical properties of the object, such as its mass, position of COM, and moment of inertia.
“Note that this does not require any prior information on the physical properties of the object, such as its mass, position of COM, and moment of inertia.”
From Catching Objects in Flight (2014) · cites this paper as [22]
The 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 [22]
The SVR-RBF method models the dynamics of the free-flying object accurately enough for this catching task but only locally; generalization could be inaccurate far away from the demonstrated states.
“To predict the trajectory of the free-flying object, the SVRRBF method [22] models the dynamics of the object accurately enough for this catching task but only locally (generalization could be inaccurate far away from the demonstrated states).”
From Catching Objects in Flight (2014) · cites this paper as [22]
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Kim, S., Shukla, A., & Billard, A. (2014) · IEEE Transactions on Robotics · doi:10.1109/tro.2014.2316022 · Cited by 220 · see citing works
Self-citation
- Authors
- Kim, S., & Billard, A.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Corresponding author
- Kim, Seungsu
- ORCID
- Billard, Aude
- Venue
- Robotics and Autonomous Systems
- Publisher
- Elsevier BV
- Published
- 2012-09
- Volume
- 60
- Issue
- 9
- Pages
- 1108-1122
- ISSN
- 0921-8890 (ISSN-L)
- OpenAlex
- W2150113360
- Cited by
- 55
- References
- 4
- Citations by year
- 2025: 6 · 2024: 7 · 2023: 5 · 2022: 6 · 2021: 3 · 2020: 4 · 2019: 1 · 2018: 2 · 2017: 5 · 2016: 7 · 2015: 5 · 2014: 3 · 2013: 1
- FWCI
- 4.17
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Robotics and Autonomous Systems (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Funders
- European Commission (248311)
- Korea Institute of Science and Technology
- FP7 Information and Communication Technologies
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Keywords
- nonlinear regression, support vector regression, Gaussian mixture regression, echo state networks, genetic programming, locally weighted projection regression, real-time motion tracking, extended Kalman filter
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