Learning grasp affordance densities
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Abstract We address the issue of learning and representing object grasp affordance models. We model grasp affordances with continuous probability density functions (grasp densities) which link object-relative grasp poses to their success probability. The underlying function representation is nonparametric and relies on kernel density estimation to provide a continuous model. Grasp densities are learned and refined from exploration, by letting a robot “play” with an object in a sequence of grasp-and-drop actions: the robot uses visual cues to generate a set of grasp hypotheses, which it then executes and records their outcomes. When a satisfactory amount of grasp data is available, an importance-sampling algorithm turns it into a grasp density. We evaluate our method in a largely autonomous learning experiment, run on three objects with distinct shapes. The experiment shows how learning increases success rates. It also measures the success rate of grasps chosen to maximize the probability of success, given reaching constraints.
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The study models the graspable space similarly to Detry et al. [11].
“Our study is similar to that of Detry et al. [11] in that we also model the graspable space.”
From Catching Objects in Flight (2014) · cites this paper as [11]
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- Authors
- Detry, R., Kraft, D., Kroemer, O., Bodenhagen, L., Peters, J., Krüger, N., & Piater, J.
- Affiliations
- KTH Royal Institute of Technology (SE)
- University of Southern Denmark (DK)
- Max Planck Institute for Biological Cybernetics (DE)
- Technische Universität Darmstadt (DE)
- Universität Innsbruck (AT)
- Corresponding author
- Detry, R.
- Venue
- Paladyn Journal of Behavioral Robotics
- Publisher
- Walter de Gruyter GmbH
- Published
- 2011-03-01
- Volume
- 2
- Issue
- 1
- Pages
- 1-17
- ISSN
- 2080-9778 (ISSN-L)
- 2081-4836
- OpenAlex
- W1971896194
- Cited by
- 79
- References
- 37
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 4 · 2022: 3 · 2021: 3 · 2020: 7 · 2019: 7 · 2018: 4 · 2017: 8 · 2016: 6 · 2015: 7 · 2014: 11 · 2013: 7 · 2012: 5
- FWCI
- 13.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Paladyn Journal of Behavioral Robotics (publishedVersion, cc-by-nc-nd)
- Lirias (acceptedVersion, other-oa)
- TUbilio (Technical University of Darmstadt) (acceptedVersion)
- Technischen Universität Darmstadt (acceptedVersion, cc-by-nc-nd)
- ORBi (University of Liège) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Max Planck Institute for Plasma Physics (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- University of Southern Denmark Research Portal (University of Southern Denmark) (submittedVersion)
- Technischen Universität Darmstadt (submittedVersion)
- Technischen Universität Darmstadt
- Funders
- Fonds De La Recherche Scientifique - FNRS
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
- Keywords
- grasp affordances, kernel density estimation, robotic grasping, importance sampling, self-directed learning
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