# US 2026/0097508 A1 · Uncertainty-aware failure detection for imitation learning robot policies > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0097508 A1 - Document ID: US20260097508A1 - Status: Published application - Kind: A1 - Application number: 19/189,531 - Filed: 2025-04-25 - Published: 2026-04-09 - Earliest claimed: 2024-10-08 - Assignees: Toyota Research Institute, Inc.; Toyota Jidosha Kabushiki Kaisha - Applicants: Toyota Research Institute, Inc. - Inventors: Chen Xu; Robert Lee; Paarth Shah; Rares A. Ambrus; Haruki Nishimura; Mikhal Itkina - Web page: https://patentgrep.com/patent/US20260097508A1 ## CPC - B25J 9/1674: {characterised by safety, monitoring, diagnostic} - B25J 9/1653: {parameters identification, estimation, stiffness, accuracy, error analysis} - B25J: MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES ({manipulators specially adapted for use in surgery A61B34/70; manipulators used in cleaning hollow articles B08B9/04}; manipulators associated with rolling mills B21B39/20; manipulators associated with forging machines B21J13/10; {manipulators associated with picking-up and placing mechanisms B23P19/007}; means for holding wheels or parts thereof B60B30/00; {vehicles with ground-engaging propulsion means, e.g. walking members B62D57/02, B62D57/032; devices for picking-up and depositing articles or materials between conveyors B65G47/90, B65G47/91; manipulators with gripping or holding means for transferring packages B65H67/065}; cranes B66C; {manipulators used in the protection or supervision of pipe-line installations F17D5/00; walking equipment adapted for nuclear steam-generators F22B37/006}; manipulators specially adapted for, or associated with, nuclear reactors G21C; {apparatus used for handling wafers during manufacture or treatment of semiconductor H10P72/50}) ## Abstract Systems and methods described herein relate to generating a discrimination scores set based on observation data and action data obtained from a robot trained to perform a task, determining a threshold value based on the discrimination score set, and comparing a discrimination score obtained while the task is being performed with the threshold value to determine if a failure condition is present. This may be performed by utilizing random network distillation or other out-of-distribution detectors and conformal band prediction computed on a set of successful rollouts by the robot. ## Claims ### Claim 1 A system, comprising: a processor; and a memory communicably coupled to the processor and storing machine-readable instructions that, when executed by the processor, cause the processor to: generate a discrimination scores set based on observation data and action data obtained from a robot trained to perform a task; determine a threshold value based on the discrimination score set; and compare a discrimination score obtained while the task is being performed with the threshold value to determine if a failure condition is present. ### Claim 2 (depends on 1) The system of claim 1, wherein the machine-readable instruction to generate the discrimination scores set is based only on the observation data and the action data from successful rollouts. ### Claim 3 (depends on 1) The system of claim 1, wherein the machine-readable instruction to generate the discrimination scores set utilizes random network distillation. ### Claim 4 (depends on 1) The system of claim 1, wherein the machine-readable instruction to determine the threshold value utilizes conformal band prediction. ### Claim 5 (depends on 1) The system of claim 1, wherein the machine-readable instructions further include to instruct the robot to perform a recovery action if the failure condition is present. ### Claim 6 (depends on 1) The system of claim 1, wherein the machine-readable instruction to generate the discrimination scores set utilizes a probabilistic approach. ### Claim 7 (depends on 5) The system of claim 5, wherein the machine-readable instruction to perform the recovery action involves selecting a new policy. ### Claim 8 A non-transitory computer-readable medium including instructions that when executed by one or more processors cause the one or more processors to: generate a discrimination scores set based on observation data and action data obtained from a robot trained to perform a task; determine a threshold value based on the discrimination score set; and compare a discrimination score obtained while the task is being performed with the threshold value to determine if a failure condition is present. ### Claim 9 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein the instruction to generate the discrimination scores set is based only on the observation data and the action data from successful rollouts. ### Claim 10 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein the instruction to generate the discrimination scores set utilizes random network distillation. ### Claim 11 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein the instruction to determine the threshold value utilizes conformal band prediction. ### Claim 12 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein the instructions further include to instruct the robot to perform a recovery action if the failure condition is present. ### Claim 13 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein the instruction to generate the discrimination scores set utilizes a probabilistic approach. ### Claim 14 A method, comprising: generating a discrimination scores set based on observation data and action data obtained from a robot trained to perform a task; determining a threshold value based on the discrimination score set; and comparing a discrimination score obtained while the task is being performed with the threshold value to determine if a failure condition is present. ### Claim 15 (depends on 14) The method of claim 14, wherein generating the discrimination scores set is based only on the observation data and the action data from successful rollouts. ### Claim 16 (depends on 14) The method of claim 14, wherein generating the discrimination scores set utilizes random network distillation. ### Claim 17 (depends on 14) The method of claim 14, wherein determining the threshold value utilizes conformal band prediction. ### Claim 18 (depends on 14) The method of claim 14, further comprising instructing the robot to perform a recovery action if the failure condition is present. ### Claim 19 (depends on 14) The method of claim 14, wherein generating the discrimination scores set utilizes a probabilistic approach. ### Claim 20 (depends on 18) The method of claim 18, wherein instructing the robot to perform the recovery action involves selecting a new policy.