Task Space Regions: A framework for pose-constrained manipulation planning
Abstract
We present a manipulation planning framework that allows robots to plan in the presence of constraints on end-effector pose, as well as other common constraints. The framework has three main components: constraint representation, constraint-satisfaction strategies, and a general planning algorithm. These components come together to create an efficient and probabilistically complete manipulation planning algorithm called the Constrained BiDirectional Rapidly-exploring Random Tree (RRT) – CBiRRT2. The underpinning of our framework for pose constraints is our Task Space Regions (TSRs) representation. TSRs are intuitive to specify, can be efficiently sampled, and the distance to a TSR can be evaluated very quickly, making them ideal for sampling-based planning. Most importantly, TSRs are a general representation of pose constraints that can fully describe many practical tasks. For more complex tasks, such as manipulating articulated objects, TSRs can be chained together to create more complex end-effector pose constraints. TSRs can also be intersected, a property that we use to plan with pose uncertainty. We provide a detailed description of our framework, prove probabilistic completeness for our planning approach, and describe several real-world example problems that illustrate the efficiency and versatility of the TSR framework.
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The general problem of generating motion plans that remain within a constraint manifold is a challenging computational problem because of the high dimensionality of the space and the complexity of the geometry.
“The general problem of generating motion plans that remain within such a constraint manifold is a challenging computational problem because of the high dimensionality of the space and the complexity of the geometry (Berenson et al., 2009; Berenson et al., 2011).”
From Lessons for Robotics From the Control Architecture of the Octopus (2022) · cites this paper as Berenson et al., 2011
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“The general problem of generating motion plans that remain within such a constraint manifold is a challenging computational problem because of the high dimensionality of the space and the complexity of the geometry (Berenson et al., 2009; Berenson et al., 2011).”
Background
- Authors
- Berenson, D., Srinivasa, S., & Kuffner, J.
- Affiliations
- Carnegie Mellon University (US)
- Intel (United States) (US)
- Corresponding author
- Berenson, Dmitry
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2011-03-15
- Volume
- 30
- Issue
- 12
- Pages
- 1435-1460
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W2116341587
- Cited by
- 368
- References
- 44
- Citations by year
- 2026: 13 · 2025: 19 · 2024: 22 · 2023: 25 · 2022: 18 · 2021: 35 · 2020: 31 · 2019: 19 · 2018: 32 · 2017: 31 · 2016: 31 · 2015: 27 · 2014: 32 · 2013: 20 · 2012: 10
- FWCI
- 22.37
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The International Journal of Robotics Research (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Control and Systems Engineering › Robotic Mechanisms and Dynamics
- Keywords
- manipulation planning, sampling-based motion planning, probabilistic completeness, constraint satisfaction, pose uncertainty, articulated object manipulation, robot motion planning
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