公开(公告)号：US20210078167A1

公开(公告)日：2021-03-18

申请号：US16886545

申请日：2020-05-28

Applicant: X Development LLC

Inventor： Seyed Mohammad Khansari Zadeh ,  Daniel Kappler ,  Jianlan Luo ,  Jeffrey Bingham ,  Mrinal Kalakrishnan

IPC: B25J9/16 ,  B25J15/00 ,  G06T7/70 ,  G06K9/00 ,  G06T7/50 ,  G06T7/90

Abstract: Generating and utilizing action image(s) that represent a candidate pose (e.g., a candidate end effector pose), in determining whether to utilize the candidate pose in performance of a robotic task. The action image(s) and corresponding current image(s) can be processed, using a trained critic network, to generate a value that indicates a probability of success of the robotic task if component(s) of the robot are traversed to the particular pose. When the value satisfies one or more conditions (e.g., satisfies a threshold), the robot can be controlled to cause the component(s) to traverse to the particular pose in performing the robotic task.

公开(公告)号：US20210316455A1

公开(公告)日：2021-10-14

申请号：US16725685

申请日：2019-12-23

Applicant: X Development LLC

Inventor： Jeffrey Bingham ,  Jianlan Luo

IPC: B25J9/16

Abstract: Cascading variational autoencoder (“VAE”) models can be used to detect robot collisions while a robot is performing a task. For a current state of the robot, various implementations include a VAE used to generate a latent space of the current state, and a predictor network used to generate a predicted latent space for the current state. A collision can be determined based on a difference between the latent space for the current state and the predicted latent space for the current state.

公开(公告)号：US11607802B2

公开(公告)日：2023-03-21

申请号：US16886545

申请日：2020-05-28

Applicant: X Development LLC

Inventor： Seyed Mohammad Khansari Zadeh ,  Daniel Kappler ,  Jianlan Luo ,  Jeffrey Bingham ,  Mrinal Kalakrishnan

IPC: B25J9/16 ,  B25J15/00 ,  G06T7/70 ,  G06K9/00 ,  G06T7/50 ,  G06T7/90 ,  G06V20/10

Abstract: Generating and utilizing action image(s) that represent a candidate pose (e.g., a candidate end effector pose), in determining whether to utilize the candidate pose in performance of a robotic task. The action image(s) and corresponding current image(s) can be processed, using a trained critic network, to generate a value that indicates a probability of success of the robotic task if component(s) of the robot are traversed to the particular pose. When the value satisfies one or more conditions (e.g., satisfies a threshold), the robot can be controlled to cause the component(s) to traverse to the particular pose in performing the robotic task.

公开(公告)号：US11285604B2

公开(公告)日：2022-03-29

申请号：US16725685

申请日：2019-12-23

Applicant: X Development LLC

Inventor： Jeffrey Bingham ,  Jianlan Luo

IPC: B25J9/16

Abstract: Cascading variational autoencoder (“VAE”) models can be used to detect robot collisions while a robot is performing a task. For a current state of the robot, various implementations include a VAE used to generate a latent space of the current state, and a predictor network used to generate a predicted latent space for the current state. A collision can be determined based on a difference between the latent space for the current state and the predicted latent space for the current state.