STOMP: Stochastic trajectory optimization for motion planning

We present a new approach to motion planning using a stochastic trajectory optimization framework. The approach relies on generating noisy trajectories to explore the space around an initial (possibly infeasible) trajectory, which are then combined to produced an updated trajectory with lower cost. A cost function based on a combination of obstacle and smoothness cost is optimized in each iteration. No gradient information is required for the particular optimization algorithm that we use and so general costs for which derivatives may not be available (e.g. costs corresponding to constraints and motor torques) can be included in the cost function. We demonstrate the approach both in simulation and on a mobile manipulation system for unconstrained and constrained tasks. We experimentally show that the stochastic nature of STOMP allows it to overcome local minima that gradient-based methods like CHOMP can get stuck in.

[1]  Q. Ye The signed Euclidean distance transform and its applications , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[2]  Geoffrey E. Hinton,et al.  Using Expectation-Maximization for Reinforcement Learning , 1997, Neural Computation.

[3]  Vijay Kumar,et al.  Motion planning for cooperating mobile manipulators , 1999, J. Field Robotics.

[4]  Geoffrey E. Hinton,et al.  Using EM for Reinforcement Learning , 2000 .

[5]  Jun Nakanishi,et al.  Learning Attractor Landscapes for Learning Motor Primitives , 2002, NIPS.

[6]  Masayuki Inaba,et al.  Motion Planning for Humanoid Robots , 2003, ISRR.

[7]  Roy Featherstone,et al.  Rigid Body Dynamics Algorithms , 2007 .

[8]  Dinesh Manocha,et al.  D-Plan: Efficient Collision-Free Path Computation for Part Removal and Disassembly , 2008 .

[9]  Tom Schaul,et al.  Fitness Expectation Maximization , 2008, PPSN.

[10]  Michael Beetz,et al.  Real-time perception-guided motion planning for a personal robot , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Siddhartha S. Srinivasa,et al.  CHOMP: Gradient optimization techniques for efficient motion planning , 2009, 2009 IEEE International Conference on Robotics and Automation.

[12]  Siddhartha S. Srinivasa,et al.  Manipulation planning with Workspace Goal Regions , 2009, 2009 IEEE International Conference on Robotics and Automation.

[13]  Nathan Ratliff,et al.  Learning to search: structured prediction techniques for imitation learning , 2009 .

[14]  Oliver Brock,et al.  BiSpace Planning: Concurrent Multi-Space Exploration , 2009 .

[15]  Siddhartha S. Srinivasa,et al.  Manipulation planning on constraint manifolds , 2009, 2009 IEEE International Conference on Robotics and Automation.

[16]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[17]  Stefan Schaal,et al.  Reinforcement learning of motor skills in high dimensions: A path integral approach , 2010, 2010 IEEE International Conference on Robotics and Automation.

[18]  Marc Toussaint,et al.  Trajectory prediction in cluttered voxel environments , 2010, 2010 IEEE International Conference on Robotics and Automation.

[19]  Victor Ng-Thow-Hing,et al.  Fast smoothing of manipulator trajectories using optimal bounded-acceleration shortcuts , 2010, 2010 IEEE International Conference on Robotics and Automation.

[20]  Sachin Chitta,et al.  Combining planning techniques for manipulation using realtime perception , 2010, 2010 IEEE International Conference on Robotics and Automation.