Online interactive perception of articulated objects with multi-level recursive estimation based on task-specific priors
Abstract
To successfully manipulate in unknown environments, a robot must be able to perceive degrees of freedom of objects in its environment. Based on the resulting kinematic model and joint configurations, the robot is able to select and adapt actions, recognize their successful completion and detect failure. We present an RGB-D-based online algorithm for the interactive perception of articulated objects. The algorithm decomposes the perception problem into three interconnected levels of recursive estimation. The estimation problems at each level are much simpler than the original problem and their robustness is improved by level-specific priors that help reject noise in the measurements. These three estimators mutually inform each other to further improve the convergence properties of the three estimation solutions. We demonstrate that the resulting algorithm is robust, accurate, and versatile in real-world experiments. We also show how the perceptual skill can be used online to control the robot's behavior in real-world manipulation tasks.
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Interactive perception has been shown effective in exploring and exploiting object and environment properties, with the main focus on rigid and articulated objects.
“IP has been shown effective in exploring and exploiting object and environment properties, with the main focus on rigid and articulated objects [2], [3], [4], [5], [6].”
From Interactive Perception for Deformable Object Manipulation (2024) · cites this paper as [4]
Cites
Speeded-Up Robust Features (SURF)
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557 · cited 1× · see citing works
“First, we reject features whose SURF descriptor [18] changes more than a”
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Interactive perception: Leveraging action in perception and perception in action
Bohg, J., Hausman, K., Sankaran, B., Brock, O., Kragic, D., Schaal, S., & Sukhatme, G. S. (2017) · IEEE Transactions on Robotics · doi:10.1109/tro.2017.2721939 · Cited by 227 · see citing works
Self-citationDitto: Building Digital Twins of Articulated Objects from Interaction
Jiang, Z., Hsu, C.-C., & Zhu, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00553 · Cited by 92 · see citing works
Active articulation model estimation through interactive perception
Hausman, K., Niekum, S., Osentoski, S., & Sukhatme, G. S. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139655 · Cited by 77 · see citing works
An integrated approach to visual perception of articulated objects
Martin-Martin, R., Hofer, S., & Brock, O. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487714 · Cited by 41 · see citing works
Self-citationSAGCI-System: Towards Sample-Efficient, Generalizable, Compositional, and Incremental Robot Learning
Lv, J., Yu, Q., Shao, L., Liu, W., Xu, W., & Lu, C. (2022) · 2022 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra46639.2022.9811859 · Cited by 22 · see citing works
Ditto in the House: Building Articulation Models of Indoor Scenes through Interactive Perception
Hsu, C.-C., Jiang, Z., & Zhu, Y. (2023) · 2023 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48891.2023.10161431 · Cited by 21 · see citing works
Kineverse: A Symbolic Articulation Model Framework for Model-Agnostic Mobile Manipulation
Rofer, A., Bartels, G., Burgard, W., Valada, A., & Beetz, M. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3146515 · Cited by 10 · see citing works
Learning to Infer Kinematic Hierarchies for Novel Object Instances
Abdul-Rashid, H., Freeman, M., Abbatematteo, B., Konidaris, G., & Ritchie, D. (2022) · 2022 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra46639.2022.9811968 · Cited by 9 · see citing works
Interactive Perception for Deformable Object Manipulation
Weng, Z., Zhou, P., Yin, H., Kravberg, A., Varava, A., Navarro-Alarcon, D., & Kragic, D. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3431943 · Cited by 7 · see citing works
“Lastly, deformable environments might entail a perception process consisting of multiple steps: the robots need to take actions for better perception which in turn bases a better action decision to take.”
Background
- Authors
- Martin Martin, R., & Brock, O.
- Affiliations
- Technische Universität Berlin (DE)
- ORCID
- Brock, Oliver
- Venue
- 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems
- Publisher
- IEEE
- Published
- 2014-09
- Pages
- 2494-2501
- ISSN
- 2153-0858 (ISSN-L)
- OpenAlex
- W2026373015
- Cited by
- 55
- References
- 1
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 5 · 2022: 11 · 2021: 8 · 2020: 4 · 2019: 4 · 2018: 6 · 2017: 5 · 2016: 4 · 2015: 3
- FWCI
- 5.54
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- interactive perception, articulated objects, recursive estimation, RGB-D, robotic manipulation, kinematic modeling, online perception, noise rejection
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