Hu et al., 2021 - Google Patents
Learning motor skills of reactive reaching and grasping of objectsHu et al., 2021
View PDF- Document ID
- 18120232436718296075
- Author
- Hu W
- Yang C
- Yuan K
- Li Z
- Publication year
- Publication venue
- 2021 IEEE International Conference on Robotics and Biomimetics (ROBIO)
External Links
Snippet
Reactive grasping of objects is an essential capability of autonomous robot manipulation, which is yet challenging to learn such sensorimotor control to coordinate coherent hand- finger motions and be robust against disturbances and failures. This work proposed a deep …
- 230000002787 reinforcement 0 abstract description 7
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1674—Programme controls characterised by safety, monitoring, diagnostic
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/40—Robotics, robotics mapping to robotics vision
- G05B2219/40053—Pick 3-D object from pile of objects
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
- B25J9/1607—Calculation of inertia, jacobian matrixes and inverses
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/39—Robotics, robotics to robotics hand
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Rahmatizadeh et al. | Vision-based multi-task manipulation for inexpensive robots using end-to-end learning from demonstration | |
Levine et al. | End-to-end training of deep visuomotor policies | |
Finn et al. | Deep spatial autoencoders for visuomotor learning | |
Rahmatizadeh et al. | From virtual demonstration to real-world manipulation using LSTM and MDN | |
Schneider et al. | Robot learning by demonstration with local gaussian process regression | |
Finn et al. | Learning visual feature spaces for robotic manipulation with deep spatial autoencoders | |
Lampe et al. | Acquiring visual servoing reaching and grasping skills using neural reinforcement learning | |
Huang et al. | Learning a real time grasping strategy | |
US20220161424A1 (en) | Device and method for controlling a robotic device | |
Rahmatizadeh et al. | Learning real manipulation tasks from virtual demonstrations using LSTM | |
Bhattacharjee et al. | A robotic system for reaching in dense clutter that integrates model predictive control, learning, haptic mapping, and planning | |
Ahmadzadeh et al. | Autonomous robotic valve turning: A hierarchical learning approach | |
Jamone et al. | Interactive online learning of the kinematic workspace of a humanoid robot | |
Skoglund et al. | Programming-by-Demonstration of reaching motions—A next-state-planner approach | |
Agarwal et al. | Dexterous functional grasping | |
Sun et al. | Motion planning and cooperative manipulation for mobile robots with dual arms | |
Ahmadzadeh et al. | Learning reactive robot behavior for autonomous valve turning | |
Xie et al. | Neural geometric fabrics: Efficiently learning high-dimensional policies from demonstration | |
Droniou et al. | Autonomous online learning of velocity kinematics on the icub: A comparative study | |
Arsenic | Developmental learning on a humanoid robot | |
Tang et al. | Deep reinforcement learning for robotics: A survey of real-world successes | |
Liang et al. | Learning preconditions of hybrid force-velocity controllers for contact-rich manipulation | |
Hu et al. | Learning motor skills of reactive reaching and grasping of objects | |
Marić et al. | Robot arm teleoperation via RGBD sensor palm tracking | |
Li et al. | Learning complex assembly skills from kinect based human robot interaction |