Specialized robot motion planning hardware and methods of making and using same are provided. A robot-specific hardware can be designed using a tool that receives a robot description comprising a collision geometry of a robot, degrees of freedom for each joint of the robot, and joint limits of the robot; receives a scenario description; generates a probabilistic roadmap (PRM) using the robot description and the scenario description; and for each edge of PRM, produces a collision detection unit comprising a circuit indicating all parts of obstacles that collide with that edge. The hardware is implemented as parallel collision detection units that provide collision detection results used to remove edges from the PRM that is searched to find a path to a goal position.

A method for planning a manipulation task of an agent, particularly a robot. The method includes: learning a number of manipulation skills wherein a symbolic abstraction of the respective manipulation skill is generated; determining a concatenated sequence of manipulation skills selected from the number of learned manipulation skills based on their symbolic abstraction so that a given goal specification indicating a given complex manipulation task is satisfied; and executing the sequence of manipulation skills.

A method for controlling a robot. The method includes providing demonstrations for performing each of a plurality of skills; training from the demonstrations, a robot trajectory model for each skill, each trajectory model is a hidden semi-Markov model having one or more initial states and one or more final states; training, from the demonstrations, a precondition model for each skill comprising, for each initial state, a probability distribution of robot configurations before executing the skill, and a final condition model for each skill comprising, for each final state, a probability distribution of robot configurations after executing the skill; receiving a description of a task, the task includes performing the skills of the plurality of skills in sequence and/or branches; generating a composed robot trajectory model; and controlling the robot according to the composed robot trajectory model to execute the task.

The present invention relates to a collision-free path generating method for a robot and an end effector quipped thereon to move. The method includes steps of configuring a virtual working environment, containing a plurality of virtual objects at least including the robot, the end effector and a target object consisting of a plurality of basic members and mapped from a working environment in a reality, in a robot simulator; selecting a level of detail and a pre-determined shape for a collider covering the plurality of virtual objects to determine boundaries for the plurality of objects; randomly sampling a combination of robot configurations; and based on the determine boundaries and the randomly sampled combination of robot configurations, performing a heuristic based pathfinding algorithm to compute a collision-free path for the robot and the end effector quipped thereon to move to the target object accordingly.

A motion path generation device generates a motion path of a robot that has a plurality of joints and a plurality of shafts. Adjacent two of the plurality of shafts are connected by a corresponding one of the plurality of joints. The motion path generation device generates a via point candidate that is a candidate of a next via point to be connected to a parent via point. The motion path generation device adds the via point candidate as a new via point in response to determining that the via point candidate does not interfere with an obstacle.

A robotic system for manipulating an object with a robotic manipulator is provided. The robotic system is configured to collect a digital representation of a task for manipulating the object; solve a robust control problem to optimize a sequence of control forces to be applied by the robotic manipulator to change a state of the object, where an evolution of the state of the object is governed by a stochastic complementarity system modeling the task with a predefined probability. The robust control problem optimizes a cost function to generate the sequence of control forces performing the task subject to joint chance constraints including a first chance constraint on the state of the object being manipulated and a second chance constraint on stochastic complementarily constraints modeling manipulation of the object. The robotic system is further configured to control the manipulation of the object based on the sequence of control forces.

To generate a more appropriate path, provided is a robot path generation device including circuitry configured to: hold a track planning module learning data set, in which a plurality of pieces of path data generated based on a motion constraint condition of a robot, and evaluation value data, which corresponds to each of the plurality of pieces path data and is a measure under a predetermined evaluation criterion, are associated with each other; and generate, based on a result of a machine learning process that is based on the track planning module learning data set, a path of the robot between a set start point and a set end point, which are freely set.

A method for planning a manipulation task of an agent, particularly a robot. The method includes: learning a number of manipulation skills wherein a symbolic abstraction of the respective manipulation skill is generated; determining a concatenated sequence of manipulation skills selected from the number of learned manipulation skills based on their symbolic abstraction so that a given goal specification indicating a given complex manipulation task is satisfied; and executing the sequence of manipulation skills.

Specialized robot motion planning hardware and methods of making and using same are provided. A robot-specific hardware can be designed using a tool that receives a robot description comprising a collision geometry of a robot, degrees of freedom for each joint of the robot, and joint limits of the robot; receives a scenario description; generates a probabilistic roadmap (PRM) using the robot description and the scenario description; and for each edge of PRM, produces a collision detection unit comprising a circuit indicating all parts of obstacles that collide with that edge. The hardware is implemented as parallel collision detection units that provide collision detection results used to remove edges from the PRM that is searched to find a path to a goal position.

The present invention relates to a collision-free path generating method for a robot and an end effector quipped thereon to move. The method includes steps of configuring a virtual working environment, containing a plurality of virtual objects at least including the robot, the end effector and a target object consisting of a plurality of basic members and mapped from a working environment in a reality, in a robot simulator; selecting a level of detail and a pre-determined shape for a collider covering the plurality of virtual objects to determine boundaries for the plurality of objects; randomly sampling a combination of robot configurations; and based on the determine boundaries and the randomly sampled combination of robot configurations, performing a heuristic based pathfinding algorithm to compute a collision-free path for the robot and the end effector quipped thereon to move to the target object accordingly.