Manipulation Planning with Probabilistic Roadmaps

By Thierry Siméon, Jean-Paul Laumond, Juan Cortés, A. Sahbani. Int. J. Robotics Res., 2004

319 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/b330572d54827974902eea7a054b226ef49095d4

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Citations in the corpus

Dynamic-Domain RRTs: Efficient Exploration by Controlling the Sampling Domain (2005, background): “Extensions of RRTs for manipulation problems and motions of closed articulated chains are developed in [8], [17], [18], [ 33 ].”
Integrated Task and Motion Planning (2020, methodology): “ther state variables such as the cooked state of a dish. This class of problems encompasses a number of problems studied in the robotics community: pick-and-place planning (21), manipulation planning (22), navigation among movable obstacles (namo) (23), and rearrangement planning (24). An important ”
Motion planning for robotic manipulation of deformable linear objects (2006, methodology): “The algorithm proposed in [19] works with continuous sets of grasps of rigid objects but plans for a single manipulator.”
Physically Embedded Planning Problems: New Challenges for Reinforcement Learning (2020, methodology): “, 2018), and progress in this area has largely been driven by the design and engineering of domain-specific hand-crafted and structured methods (Dantam et al., 2016; Kaelbling and Lozano-Pérez, 2011; Plaku and Hager, 2010; Siméon et al., 2004; Srivastava et al., 2014; Toussaint, 2015), which typical”
Learning to Manipulate Articulated Objects in Unstructured Environments Using a Grounded Relational Representation (2008, background): “These manipulation planners address various flavors of manipulation, including grasping and in-hand manipulation [24], manipulation with sliding contacts [26], non-prehensile manipulation [1, 14], and gross motion planning for manipulation [21].”
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms (2019, methodology): “One common use of hybrid models is to represent modes in manipulation tasks [145, 345, 219, 232, 65, 438].”
Manipulation Planning for Deformable Linear Objects (2007, methodology): “The algorithm proposed in [26] works with continuous sets of grasps of rigid objects, but plans the motion of a single manipulator, with the regrasps being done by releasing the object on a horizontal surface.”
Deliberation for autonomous robots: A survey (2017, background): “Manipulation planning requires finding feasible sequences of grasping positions, each of which is a partial constraint on the robot configuration that changes its kinematics [198].”
Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning (2018, background): “To relate to [26, 10, 32], the path constraints define the piece-wise smooth manifolds of feasible paths with local tangent spaces TzX = {ẋ(t) | x(t) = z, x feasible} at configuration z ∈ X; a kinematic mode is a phase during which the path stays within a smooth manifold.”