GENERATING ROBOT TRAJECTORIES USING NEURAL NETWORKS

1 . A method of generating a trajectory of a robot, the method comprising:

receiving a plurality of path points;

processing each network input of a plurality of network inputs in an input sequence that is derived from the path points using a trajectory generation neural network to generate an output sequence comprising a plurality of network outputs, each network output specifying a respective displacement between two adjacent trajectory points; and

generating, based on the output sequence, a predicted trajectory of the robot.

2 . The method of claim 1 , wherein the predicted trajectory of the robot represents a prediction for an output trajectory of a closed trajectory generator when given the path points.

3 . The method of claim 1 , wherein each network input specifies (i) a position of a current trajectory point, (ii) a current reference direction of the current trajectory point, (iii) a future reference direction of the current trajectory point, and (iv) a goal vector measuring a displacement between the current trajectory point and a current path point.

4 . The method of claim 1 , further comprising:

generating an adjusted predicted trajectory from the predicted trajectory, comprising, for each network output in the output sequence:

determining whether the displacement that is specified by the network output is parallel to the reference direction of the current trajectory point; and

in response to a positive determination:

determining, based on two adjacent path points of the current trajectory point, an adjustment to the displacement.

5 . The method of claim 1 , wherein:

the trajectory generation neural network is a recurrent neural network; and

generating the output sequence comprising the plurality of network outputs comprises, at each of a plurality of time steps:

processing, using the trajectory generation neural network, a current network input and a preceding network output to generate a current network output.

6 . The method of claim 4 , wherein determining the adjustment to the displacement comprises:

projecting the displacement to a line connecting two adjacent path points of the current trajectory point.

7 . The method of claim 4 , wherein determining the adjustment to the displacement further comprises:

iteratively determining adjustments to respective displacements specified by preceding network outputs in the output sequence.

8 . The method of claim 4 , further comprising:

generating a smoothened predicted trajectory by computing a weighted average of the predicted trajectory and the adjusted predicted trajectory.

9 . The method of claim 1 , wherein each trajectory point or path point is represented by multi-dimensional data having a respective dimension that is dependent on degrees of freedom (DoF) of the robot.

10 . The method of claim 1 , further comprising:

training the trajectory generation neural network by optimizing an objective function measuring a difference between network outputs and target outputs that are derived from trajectories generated by Robot Controller Simulation (RCS).

11 . A system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations for generating a trajectory of a robot, the operations comprising:

receiving a plurality of path points;

processing each network input of a plurality of network inputs in an input sequence that is derived from the path points using a trajectory generation neural network to generate an output sequence comprising a plurality of network outputs, each network output specifying a respective displacement between two adjacent trajectory points; and

generating, based on the output sequence, a predicted trajectory of the robot.

12 . The system of claim 11 , wherein each network input specifies (i) a position of a current trajectory point, (ii) a current reference direction of the current trajectory point, (iii) a future reference direction of the current trajectory point, and (iv) a goal vector measuring a displacement between the current trajectory point and a current path point.

13 . The system of claim 11 , wherein the operations further comprise:

generating an adjusted predicted trajectory from the predicted trajectory, comprising, for each network output in the output sequence:

determining whether the displacement that is specified by the network output is parallel to the reference direction of the current trajectory point; and

in response to a positive determination:

determining, based on two adjacent path points of the current trajectory point, an adjustment to the displacement.

14 . The system of claim 11 , wherein:

the trajectory generation neural network is a recurrent neural network; and

generating the output sequence comprising the plurality of network outputs comprises, at each of a plurality of time steps:

processing, using the trajectory generation neural network, a current network input and a preceding network output to generate a current network output.

15 . The system of claim 13 , wherein the operations further comprise:

generating a smoothened predicted trajectory by computing a weighted average of the predicted trajectory and the adjusted predicted trajectory.

16 . One or more non-transitory computer-readable storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations for generating a trajectory of a robot, the operations comprising:

receiving a plurality of path points;

processing each network input of a plurality of network inputs in an input sequence that is derived from the path points using a trajectory generation neural network to generate an output sequence comprising a plurality of network outputs, each network output specifying a respective displacement between two adjacent trajectory points; and

generating, based on the output sequence, a predicted trajectory of the robot.

17 . The non-transitory computer-readable storage media of claim 16 , wherein each network input specifies (i) a position of a current trajectory point, (ii) a current reference direction of the current trajectory point, (iii) a future reference direction of the current trajectory point, and (iv) a goal vector measuring a displacement between the current trajectory point and a current path point.

18 . The non-transitory computer-readable storage media of claim 16 , wherein the operations further comprise:

generating an adjusted predicted trajectory from the predicted trajectory, comprising, for each network output in the output sequence:

determining whether the displacement that is specified by the network output is parallel to the reference direction of the current trajectory point; and

in response to a positive determination:

determining, based on two adjacent path points of the current trajectory point, an adjustment to the displacement.

19 . The non-transitory computer-readable storage media of claim 16 , wherein:

the trajectory generation neural network is a recurrent neural network; and

generating the output sequence comprising the plurality of network outputs comprises, at each of a plurality of time steps:

processing, using the trajectory generation neural network, a current network input and a preceding network output to generate a current network output.

20 . The non-transitory computer-readable storage media of claim 16 , wherein the operations further comprise:

generating a smoothened predicted trajectory by computing a weighted average of the predicted trajectory and the adjusted predicted trajectory.