公开(公告)号：US20250108512A1

公开(公告)日：2025-04-03

申请号：US18825458

申请日：2024-09-05

Applicant: Tata Consultancy Services Limited

Inventor： Rolif LIMA ,  Vismay Dilipkumar VAKHARIA ,  Utsav RAI ,  Hardik Rajiv MEHTA ,  Aditya CHOUDHARY ,  Amitkumar Vinodbhai PARMAR ,  Kaushik DAS ,  Vighnesh VATSAL

IPC: B25J9/16 ,  G05B19/4155 ,  G06F3/01

Abstract: This disclosure relates to system and method to reconstruct human motion for mobile robot teleoperation using shared control. The method of the present disclosure acquire an input feed of a human operator to perform a task with assistance in a remote environment using shared control. The mobile robot reconstructs to follow a trajectory of the human operator towards the intended goal in the remote environment. The mobile robot determines at least one goal intended by the human operator based on a previously occurred state, a current kinematic state and a future trajectory of the human operator and a known position of the plurality of goals. The model predictive control generates at least one instruction to control the movement of the mobile robot to perform at least one of following the trajectory of the human operator and reaching the operator intended goal based on a joint angle position and a velocity.

公开(公告)号：US20230373096A1

公开(公告)日：2023-11-23

申请号：US18198661

申请日：2023-05-17

Applicant: Tata Consultancy Services Limited

Inventor： Jayavardhana Rama GUBBI LAKSHMINARASIMHA ,  Vartika SENGAR ,  Vighnesh VATSAL ,  Balamuralidhar PURUSHOTHAMAN ,  Arpan PAL ,  Nijil GEORGE ,  Aditya KAPOOR

IPC: B25J9/16

CPC classification number: B25J9/1697 ,  B25J9/161 ,  B25J9/163 ,  B25J9/1653

Abstract: Conventional task planners assume that the task-plans provided are executable, hence these are not task-aware. Present disclosure alleviates the downward refinability assumption, that is, planning can be decomposed separate symbolic and continuous planning steps by introducing bi-level planning, a plan which is a series of actions that the robot needs to take to achieve the goal task is curated. Firstly, abstract symbolic actions are converted to continuous vectors and used therein to enable interaction with an environment. Images of objects placed in the environment are captured and concepts are learnt from the captured images and attributes of objects are detected. A hierarchical scene graph is generated from the concepts and attributes wherein the graph includes interpretable sub-symbolic representations and from these interpretable symbolic representations are obtained for identifying goal task. Anomalies are detected from the scene graph and robotic actions are generated to correct the detected anomalies.