公开(公告)号：US20250018562A1

公开(公告)日：2025-01-16

申请号：US18359550

申请日：2023-07-26

Applicant: GOOGLE LLC

Inventor： Fei Xia ,  Harris Chan ,  Brian Ichter ,  Wenlong Huang ,  Ted Xiao ,  Karol Hausman

IPC: B25J9/16 ,  B25J13/00 ,  B25J13/08 ,  B25J19/02 ,  G06F40/40

Abstract: Some implementations related to using a large language model (LLM) in generating (and potentially refining) a plan for the execution of a long-horizon robotic task. Various implementations include processing, using the LLM, a free-form natural language instruction and textual feedback to generate LLM output. In many implementations, the free-form natural language instruction describes the robotic task. In additional or alternative implementations, the textual feedback can include task-specific feedback, passive scene description feedback, active scene description feedback, one or more additional or alternative types of environmental feedback, and/or combinations thereof. In some implementations, the system can select one or more robotic skills to perform based on the LLM output.

公开(公告)号：US20230311335A1

公开(公告)日：2023-10-05

申请号：US18128953

申请日：2023-03-30

Applicant: GOOGLE LLC

Inventor： Karol Hausman ,  Brian Ichter ,  Sergey Levine ,  Alexander Toshev ,  Fei Xia ,  Carolina Parada

IPC: B25J13/00 ,  B25J11/00 ,  B25J9/16 ,  G06F40/40

CPC classification number: B25J13/003 ,  B25J11/0005 ,  B25J9/163 ,  B25J9/161 ,  G06F40/40

Abstract: Implementations process, using a large language model, a free-form natural language (NL) instruction to generate to generate LLM output. Those implementations generate, based on the LLM output and a NL skill description of a robotic skill, a task-grounding measure that reflects a probability of the skill description in the probability distribution of the LLM output. Those implementations further generate, based on the robotic skill and current environmental state data, a world-grounding measure that reflects a probability of the robotic skill being successful based on the current environmental state data. Those implementations further determine, based on both the task-grounding measure and the world-grounding measure, whether to implement the robotic skill.