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We introduce the Riemannian Motion Policy (RMP), a new mathematical object for modular motion generation.
A unified approach for motion and force control of robot manipulators: The operational space formulation
O. Khatib · 1987
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Optimal Control and Estimation
R. Stengel · 1994
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Natural gradient works efficiently in learning
Shun-Ichi Amari · 1998
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Covariant policy search
J. Andrew (Drew) Bagnell and Jeff Schneider · 2003
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Planning Algorithms
Steven M. LaValle · 2006
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Numerical Optimization
Jorge Nocedal and Stephen Wright · 2006
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Methods of Information Geometry
Shun-Ichi Amari and Hiroshi Nagaoka · 2007
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Movement reproduction and obstacle avoidance with dynamic movement primitives and potential fields
Dae-Hyung Park, Heiko Hoffmann, Peter Pastor, and Stefan Schaal · 2008
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CHOMP: Gradient optimization techniques for efficient motion planning
Nathan Ratliff, Matthew Zucker, J. Andrew (Drew) Bagnell, and Siddhartha Srinivasa · 2009
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Robot trajectory optimization using approximate inference
Marc Toussaint · 2009
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Real-time reactive motion generation based on variable attractor dynamics and shaped velocities
S. Haddadin, H. Urbanek, S. Parusel, D. Burschka, J. Roßmann, A. Albu-Schäffer, and G. Hirzinger · 2010
Cited alongside, same era.
A Bayesian view on motor control and planning
Marc Toussaint and Christian Goerick · 2010
Cited alongside, same era.
STOMP: Stochastic trajectory optimization for motion planning
Mrinal Kalakrishnan, Sachin Chitta, Evangelos Theodorou, Peter Pastor, and Stefan Schaal · 2011
Cited alongside, same era.
ITOMP: Incremental trajectory optimization for real-time replanning in dynamic environments
Jia Pan Chonhyon Park and Dinesh Manocha · 2012
Cited alongside, same era.
Finding locally optimal, collision-free trajectories with sequential convex optimization
John D. Schulman, Jonathan Ho, Alex Lee, Ibrahim Awwal, Henry Bradlow, and Pieter Abbeel · 2013
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Collision avoidance with potential fields based on parallel processing of 3d-point cloud data on the gpu
K. B. Kaldestad, S. Haddadin, R. Belder, G. Hovland, and D. A. Anisi · 2014
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Batch informed trees (bit*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs
Jonathan Gammell, Siddhartha Srinivasa, and Timothy Barfoot · 2015
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Understanding the geometry of workspace obstacles in motion optimization
Nathan Ratliff, Marc Toussaint, and Stefan Schaal · 2015
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Warping the workspace geometry with electric potentials for motion optimization of manipulation tasks
Jim Mainprice, Nathan Ratliff, and Stefan Schaal · 2016
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F. Flacco, T. Kröger, A. De Luca, and O. Khatib · 2012
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A dynamical system approach to realtime obstacle avoidance
S.-M. Khansari-Zadeh and A. Billard · 2012
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An integrated system for real-time model-predictive control of humanoid robots
Tom Erez, Kendall Lowrey, Yuval Tassa, Vikash Kumar, Svetoslav Kolev, and Emanuel Todorov · 2013
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Dynamical movement primitives: Learning attractor models for motor behaviors
A. J. Ijspeert, J. Nakanishi, H. Hoffmann, P. Pastor, and S. Schaal · 2013
Cited alongside, same era.
A novel augmented lagrangian approach for inequalities and convergent any-time non-central updates
Marc Toussaint
Cited in the paper.
Newton methods for k-order Markov constrained motion problems
Marc Toussaint
Cited in the paper.
Real-time perception meets reactive motion generation
Daniel Kappler, Franziska Meier, Jan Issac, Jim Mainprice, Cristina Garcia Cifuentes, Manuel Wüthrich, Vincent Berenz, Stefan Schaal, Nathan Ratliff, and Jeannette Bohg · 2017
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Approximately optimal continuous-time motion planning and control via probabilistic inference
Mustafa Mukadam, Ching-An Cheng, Xinyan Yan, and Byron Boots · 2017
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Probabilistic prioritization of movement primitives
A. Paraschos, R. Lioutikov, J. Peters, and G. Neumann · 2017
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