RTRBench Listing of Benchmarks

Following, a listing of RTRBench kernels is included. Follow the links for the kernels to get more details, including the specifications of the code and data. We break the kernels into three groups based on their stage in robots’ software pipeline.

Perception

• 01.pfl
  Robot localization using Particle Filter (PF).
  [Code] [Doc]

• 02.ekfslam
  Robot localization and mapping using Extended Kalman Filter (EKF).
  [Code] [Doc]

• 03.srec
  Scene reconstruction using the Iterative Closest Point (ICP) algorithm.
  [Code] [Doc]

Planning

• 04.pp2d
  Mobile robot path planning using the A* algorithm in 2D environments.
  [Code] [Doc]

• 05.pp3d
  Mobile robot path planning using the A* algorithm in 3D environments.
  [Code] [Doc]

• 06.movtar
  Mobile robot path planning using the A* algorithm with a moving target.
  [Code] [Doc]

• 07.prm
  Stationary robotic arm planning using the Probabilistic RoadMap (PRM) algorithm.
  [Code] [Doc]

• 08.rrt
  Stationary robotic arm planning using the Rapidly-exploring Random Tree (RRT) algorithm.
  [Code] [Doc]

• 09.rrtstar
  Stationary robotic arm planning using the Rapidly-exploring Random Tree Star (RRT*) algorithm.
  [Code] [Doc]

• 10.rrtpp
  Stationary robotic arm planning using the Rapidly-exploring Random Tree (RRT) algorithm with path shortcutting.
  [Code] [Doc]

• 11.sym-blkw
  Solving Blocksworld problem using symbolic planning.
  [Code] [Doc]

• 12.sym-fext
  Solving a firefighting problem using symbolic planning.
  [Code] [Doc]

Control

• 13.dmp
  Path tracking using Dynamic Movement Primitives (DMP).
  [Code] [Doc]

• 14.mpc
  Path tracking using Model Predictive Control (MPC).
  [Code] [Doc]

• 15.cem
  Reinforcement learning using Cross-Entropy Method (CEM).
  [Code] [Doc]

• 16.bo
  Reinforcement learning using Bayesian Optimization (BO).
  [Code] [Doc]