LL4MA lab

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~~NOCACHE~~
====== Software ======
All of our software is hosted on [[https://bitbucket.org/robot-learning/ | our bitbucket page]], including code from our [[https://robot-learning.cs.utah.edu/publications| published papers]], any infrastructure code needed to interact with the robots we use in our lab, and much more. Here we provide high-level descriptions of our packages and links to wiki pages that offer more details on each package.

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===== Robot Perception =====
^ Package ^ Description ^ Resources ^
| [[https://bitbucket.org/robot-learning/in_depth_saliency| in_depth_saliency]] | Software to compute visual saliency from RGB-D images | [ [[http://www.cs.utah.edu/~thermans/papers/ciptadi-bmvc2013.pdf| paper]] ] [ [[http://www.cc.gatech.edu/cpl/projects/depth_saliency/| data]] ] |

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===== Robot Control, Dynamics, and Kinematics =====

^ Package ^ Description ^ Resources ^
| [[https://bitbucket.org/robot-learning/ll4ma_kdl | ll4ma_kdl]] | Kinematic and dynamics wrapper for [[http://www.orocos.org/wiki/orocos/kdl-wiki|KDL]] with helper functions. | [ [[https://bitbucket.org/robot-learning/ll4ma_kdl|source]] ] |
| [[https://bitbucket.org/robot-learning/ll4ma_robot_control | ll4ma_robot_control]] | Controllers for real and simulated robots. | [ [[https://bitbucket.org/robot-learning/ll4ma_robot_control|source]] ] |
| [[https://bitbucket.org/robot-learning/ll4ma_robot_interface | ll4ma_robot_interface]] | Abstraction layer for communicating with various real and simulated robots. | [ [[https://bitbucket.org/robot-learning/ll4ma_robot_interface|source]] ] |

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===== Robot Infrastructure =====
^ Package ^ Description ^ Resources ^
| [[https://bitbucket.org/robot-learning/ll4ma_robots_description|ll4ma_robots_description]] | URDF and mesh resources for robot and environment models. | [ [[https://bitbucket.org/robot-learning/ll4ma_robots_description|source]] ] |


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===== Simulation =====

^ Package ^ Description ^ Resources ^
| [[https://bitbucket.org/robot-learning/ll4ma_robots_gazebo | ll4ma_robots_gazebo]] | Simulation of robots that we have in our lab using [[http://gazebosim.org/|Gazebo]]. | [ [[https://bitbucket.org/robot-learning/ll4ma_robots_gazebo|source]] ] [ [[ll4ma_robots_gazebo|wiki]] ] |

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===== Calibration =====
^ Package ^ Description ^ Resources ^
| [[https://bitbucket.org/robot-learning/robot_aruco_calibration|robot_aruco_calibration]] | Simple robot-camera calibration  using [[https://www.uco.es/investiga/grupos/ava/node/26|ArUco Markers]]. | [ [[https://bitbucket.org/robot-learning/robot_aruco_calibration|source]] ] |

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===== Data Collection =====
^ Package ^ Description ^ Resources ^
| [[https://bitbucket.org/robot-learning/rlbench_data_collection/src/master/ | rlbench_data_collection]] | Utilities for collecting data from the [[https://github.com/stepjam/RLBench|RLBench framework]]. | [ [[https://bitbucket.org/robot-learning/rlbench_data_collection/src/master/ | source]] ] |


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===== Hardware Drivers =====
^ Package ^ Description ^ Resources ^
| [[https://bitbucket.org/robot-learning/optoforce_etherdaq|optoforce_etherdaq]] | Fork of [[https://github.com/OptoForce/etherdaq_ros|optoforce_etherdaq]] with additional ROS services. | [ [[https://bitbucket.org/robot-learning/optoforce_etherdaq|source]] ] |
| [[https://bitbucket.org/robot-learning/phantom_omni|phantom_omni]] | Fork of [[https://github.com/fsuarez6/phantom_omni|phantom_omni]] adapted with our URDF model. | [ [[https://bitbucket.org/robot-learning/phantom_omni|source]] ] |

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===== Research Code =====
==== Grasp Planning ====
^ Package ^ Description ^ Resources ^
| [[https://robot-learning.cs.utah.edu/project/grasp_inference#source_code_data | grasp_inference]] | Planning Multi-Fingered Grasps as Probabilistic Inference in a Learned Deep Network. ISRR 2017. | [ [[http://www.cs.utah.edu/~thermans/papers/lu-isrr2017-deep-multifinger-grasping.pdf|paper]] ] [ [[https://robot-learning.cs.utah.edu/project/grasp_inference|project page]]] |
==== In-hand Manipulation ====
^ Package ^ Description ^ Resources ^
| [[https://bitbucket.org/robot-learning/relaxed_rigidity_in_grasp | in_grasp]] | Relaxed-Rigidity Constraints: In-Grasp Manipulation using Purely Kinematic Trajectory Optimization, RSS 2017| [ [[https://robot-learning.cs.utah.edu/_media/project/sundaralingam_rss_2017-in-grasp-opt.pdf|paper]] ] [ [[https://robot-learning.cs.utah.edu/project/in_hand_manipulation|project page]]] |