[go: up one dir, main page]

Platt Jr et al., 2010 - Google Patents

Belief space planning assuming maximum likelihood observations

Platt Jr et al., 2010

View PDF
Document ID
17623891902276901105
Author
Platt Jr R
Tedrake R
Kaelbling L
Lozano-Perez T
Publication year

External Links

Snippet

We cast the partially observable control problem as a fully observable underactuated stochastic control problem in belief space and apply standard planning and control techniques. One of the difficulties of belief space planning is modeling the stochastic …
Continue reading at dspace.mit.edu (PDF) (other versions)

Classifications

    • 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
    • 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
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • 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
    • 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
    • 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
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • 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
Platt Jr et al. Belief space planning assuming maximum likelihood observations
Brito et al. Where to go next: Learning a subgoal recommendation policy for navigation in dynamic environments
Bandyopadhyay et al. Intention-aware motion planning
Franceschetti et al. Robotic arm control and task training through deep reinforcement learning
Van Den Berg et al. LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information
Bautin et al. Inevitable collision states: A probabilistic perspective
Ma et al. Bi-Risk-RRT based efficient motion planning for autonomous ground vehicles
Venkatraman et al. Improved learning of dynamics models for control
King et al. Unobservable monte carlo planning for nonprehensile rearrangement tasks
Chakravorty et al. Generalized sampling-based motion planners
Lahijanian et al. Resource-performance tradeoff analysis for mobile robots
Llopis-Albert et al. Optimization approaches for robot trajectory planning
EP3955080B1 (en) Method and device for socially aware model predictive control of a robotic device using machine learning
Chen et al. An improved artificial potential field based path planning algorithm for unmanned aerial vehicle in dynamic environments
Mohanty et al. Application of deep Q-learning for wheel mobile robot navigation
Hernández et al. A review of path planning and control for autonomous robots
Rafieisakhaei et al. Feedback motion planning under non-gaussian uncertainty and non-convex state constraints
Shirai et al. Robust pivoting manipulation using contact implicit bilevel optimization
Dunlap et al. Nonlinear model predictive control using sampling and goal-directed optimization
Wu et al. Robust-RRT: Probabilistically-complete motion planning for uncertain nonlinear systems
Toner et al. Probabilistically safe mobile manipulation in an unmodeled environment with automated feedback tuning
Rahul et al. Deep reinforcement learning with inverse jacobian based model-free path planning for deburring in complex industrial environment
Lopes et al. Learning complex behaviors via sequential composition and passivity-based control
Suzuki et al. Posture evaluation for mobile manipulators using manipulation ability, tolerance on grasping, and pose error of end-effector
Sintov et al. Motion planning with competency-aware transition models for underactuated adaptive hands