IP Library › Granted Patent US 12,661,785
Granted Patent B2
US 12,661,785 · App. 18/429,595 · Granted Jun 23, 2026

Robot control method and apparatus, robot, and storage medium

Inventors: Cheng Zhou (Shenzhen, CN); Yu Zheng (Shenzhen, CN)
Assignee: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
B25J9/1664B25J9/04B25J9/1669
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,661,785
App. No.
18/429,595
Filed
Feb 1, 2024
Granted
Jun 23, 2026
Kind
B2
Art Unit
3656
USPC
700/245
Abstract

A robot control method includes: controlling an end effector of the robot to collide with a target object in a process of the end effector moving to the target object; controlling the end effector to rotate relative to the target object, to adjust the target object to a target pose corresponding to a pick-up action; and controlling the end effector to pick up the target object after the target object is adjusted to the target pose; wherein the end effector is in motion during an entire phase from the end effector colliding with the target object to picking up the target object.

Claims (47)

1 . A robot control method, performed by a robot, and the method comprising:

controlling an end effector of the robot to collide with a target object in a process of the end effector moving to the target object;

controlling the end effector to rotate relative to the target object, to adjust the target object to a target pose corresponding to a pick-up action;

controlling the end effector to pick up the target object after the target object is adjusted to the target pose; wherein the end effector is in motion during an entire phase from the end effector colliding with the target object to picking up the target object;

obtaining, based on a unified equation of state corresponding to a hybrid system comprising the end effector and the target object, pose information and force information of the hybrid system at each time step of the entire phase; and

controlling the end effector at each time step of the entire phase according to the pose information and the force information of the hybrid system at each time step of the entire phase.

2 . The method according to claim 1 , wherein a position where the end effector collides with the target object is located at a bottom of the target object, to allow the target object to fall toward the end effector.

3 . The method according to claim 2 , wherein the controlling the end effector to rotate relative to the target object comprises:

controlling the end effector to rotate in a direction opposite to a falling direction in a process of the target object falling over.

4 . The method according to claim 1 , wherein a pick-up mode of the end effector is non-grasping type in which the target object is held by a palm part of the end effector, and a static friction force on a bearing surface of the end effector allows the target object and the end effector to remain relatively stationary.

5 . The method according to claim 4 , wherein the controlling the end effector to pick up the target object comprises:

controlling the end effector to rotate until the target object and the palm part of the end effector is relatively stationary.

6 . The method according to claim 1 , wherein a pick-up mode of the end effector is grasping type in which a finger part of the end effector is closed toward a palm part of the end effector, to grasp the target object between the palm part and the finger part.

7 . The method according to claim 1 , wherein the pose information and the force information of the hybrid system at each time step of the entire phase meet the following constraint conditions:

an inequality constraint condition, configured to constrain a friction force of a contact position between the end effector and the target object; and

an equality linkage constraint condition, configured to constrain a motion trajectory and an acting force between the end effector and the target object.

8 . The method according to claim 7 , wherein the obtaining, based on the unified equation of state corresponding to the hybrid system comprising the end effector and the target object, the pose information and the force information of the hybrid system at each time step of the entire phase comprises:

constructing an objective function based on a Bellman optimal equation, the unified equation of state, and the constraint conditions; and

solving the objective function by using a primal dual augmented Lagrangian multiplier method, to obtain the pose information and the force information of the hybrid system at each time step of the entire phase.

9 . The method according to claim 1 , wherein the controlling the end effector of the robot to collide with the target object comprises:

obtaining a change amount of a contact velocity between the end effector and the target object before and after a collision of the end effector and the target object, wherein the contact velocity refers to a relative velocity of a contact point between the end effector and the target object;

determining a velocity of the target object after the collision according to the change amount and a velocity of the target object before the collision;

determining a velocity that the end effector needs to reach before the collision, according to the velocity of the target object after the collision, a maximum acceleration of the end effector, and a constraint relationship between the velocity of the target object after the collision and velocities of the end effector before and after the collision; and

controlling the end effector to collide with the target object according to the velocity that the end effector needs to reach before the collision.

10 . A robot control apparatus, comprising: a processor and a memory, the memory storing a computer program, and the computer program being loaded and executed by the processor to implement:

controlling an end effector of the robot to collide with a target object in a process of the end effector moving to the target object;

controlling the end effector to rotate relative to the target object, to adjust the target object to a target pose corresponding to a pick-up action;

controlling the end effector to pick up the target object after the target object is adjusted to the target pose; wherein the end effector is in motion during an entire phase from the end effector colliding with the target object to picking up the target object;

obtaining, based on a unified equation of state corresponding to a hybrid system comprising the end effector and the target object, pose information and force information of the hybrid system at each time step of the entire phase; and

controlling the end effector at each time step of the entire phase according to the pose information and the force information of the hybrid system at each time step of the entire phase.

11 . The apparatus according to claim 10 , wherein a position where the end effector collides with the target object is located at a bottom of the target object, to allow the target object to fall toward the end effector.

12 . The apparatus according to claim 11 , wherein the rotation control module is further configured to control the end effector to rotate in a direction opposite to a falling direction in a process of the target object falling over.

13 . The apparatus according to claim 10 , wherein a pick-up mode of the end effector is non-grasping type in which the target object is held by a palm part of the end effector, and a static friction force based on a bearing surface of the end effector allows the target object and the end effector to remain relatively stationary.

14 . The apparatus according to claim 13 , wherein the pick-up control module is further configured to control the end effector to rotate until the target object and the palm part of the end effector is relatively stationary.

15 . The apparatus according to claim 10 , wherein a pick-up mode of the end effector is grasping type in which a finger part of the end effector is closed toward a palm part of the end effector, to grasp the target object between the palm part and the finger part.

16 . The apparatus according to claim 10 , wherein the pose information and the force information of the hybrid system at each time step of the entire phase meet the following constraint conditions:

an inequality constraint condition, configured to constrain a friction force of a contact position between the end effector and the target object; and

an equality linkage constraint condition, configured to constrain a motion trajectory and an acting force between the end effector and the target object.

17 . The apparatus according to claim 16 , wherein the obtaining, based on the unified equation of state corresponding to the hybrid system comprising the end effector and the target object, the pose information and the force information of the hybrid system at each time step of the entire phase comprises:

constructing an objective function based on a Bellman optimal equation, the unified equation of state, and the constraint conditions; and

solving the objective function by using a primal dual augmented Lagrangian multiplier method, to obtain the pose information and the force information of the hybrid system at each time step of the entire phase.

18 . A non-transitory computer-readable storage medium, storing a computer program, the computer program being loaded and executed by a processor coupled to a robot to implement:

controlling an end effector of the robot to collide with a target object in a process of the end effector moving to the target object;

controlling the end effector to rotate relative to the target object, to adjust the target object to a target pose corresponding to a pick-up action;

controlling the end effector to pick up the target object after the target object is adjusted to the target pose; wherein the end effector is in motion during an entire phase from the end effector colliding with the target object to picking up the target object;

obtaining, based on a unified equation of state corresponding to a hybrid system comprising the end effector and the target object, pose information and force information of the hybrid system at each time step of the entire phase; and

controlling the end effector at each time step of the entire phase according to the pose information and the force information of the hybrid system at each time step of the entire phase.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 1, 2024
From: ZHOU, CHENG; ZHENG, YU
To: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
Reel/Frame 066323/0069 →
Priority Claims (1)
CN 202211348498.1 · Oct 31, 2022 · national
Continuity (2)
Continuation PCTCN2023118082 · Sep 11, 2023
Related Publication 20240173862A1 · May 30, 2024
References Cited (56)
US 9827670B1 · Strauss · 2017 [cited by examiner]
US 10131051B1 · Goyal et al. · 2018 [cited by applicant]
US 20120072022A1 · Kim et al. · 2012 [cited by applicant]
US 20130184870A1 · Ota · 2013 [cited by examiner]
US 20170274539A1 · Maeda · 2017 [cited by examiner]
US 20200114508A1 · Kawabata et al. · 2020 [cited by applicant]
US 20210122039A1 · Su · 2021 [cited by examiner]
US 20210146532A1 · Rodriguez et al. · 2021 [cited by applicant]
US 20210245365A1 · Sugahara et al. · 2021 [cited by applicant]
US 20210291366A1 · Eto · 2021 [cited by examiner]
US 20220297294A1 · Baek · 2022 [cited by examiner]
US 20250170718A1 · Isobe · 2025 [cited by examiner]
CN 107414825A · 2017 [cited by applicant]
CN 207669311U · 2018 [cited by applicant]
CN 110636924A · 2019 [cited by applicant]
CN 111015655A · 2020 [cited by applicant]
CN 111225554A · 2020 [cited by applicant]
CN 113246139A · 2021 [cited by applicant]
CN 115194771A · 2022 [cited by applicant]
CN 115958587A · 2023 [cited by applicant]
EP 1340700A1 · 2003 [cited by applicant]
JP 2022155623A · 2022 [cited by applicant]
China National Intellectual Property Administration (CNIPA) Office Action 1 for Application No. 202211348498.1 Mar. 19, 2025 12 Pages (including translation). [cited by applicant]
Fabio Ruggiero et al., “Nonprehensile dynamic manipulation: A survey.” IEEE Robotics and Automation Letters 3.3 (2018): 1711-1718. [cited by applicant]
Kevin M. Lynch et al. “The roles of shape and motion in dynamic manipulation: The butterfly example.” Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No. 98CH36146). vol. 3. IEEE, 1998. … [cited by applicant]
Matthew T. Mason et al., “Dynamic manipulation.” Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'93). vol. 1. IEEE, 1993. pp. 152-159. [cited by applicant]
Raymond R. Ma et al., “On dexterity and dexterous manipulation.” 2011 15th International Conference on Advanced Robotics (ICAR). IEEE, 2011. pp. 1-7. [cited by applicant]
Aude Billard et al., “Trends and challenges in robot manipulation.” Science 364.6446 (2019): eaat8414. [cited by applicant]
Jochen Stuber et al., “Let's push things forward: A survey on robot pushing.” Frontiers in Robotics and AI 7 (2020): 8. [cited by applicant]
Diana Serra et al. “Control of nonprehensile planar rolling manipulation: A passivity-based approach.” IEEE Transactions on Robotics 35.2 (2019): 317-329. [cited by applicant]
Hitoshi Arisumi et al., “Casting manipulation—Midair control of a gripper by impulsive force.” IEEE transactions on robotics 24.2 (2008): 402-415. [cited by applicant]
J. Zachary Woodruff et al., “Planning and control for dynamic, nonprehensile, and hybrid manipulation tasks.” 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017. pp. 4066-4073. [cited by applicant]
Hitoshi Arisumi et al., “Whole-body motion of a humanoid robot for passing through a door-opening a door by impulsive force.” 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2009. pp. 428… [cited by applicant]
Thomas Wimbock et al. “Comparison of object-level grasp controllers for dynamic dexterous manipulation.” The International Journal of Robotics Research 31.1 (2012): 3-23. [cited by applicant]
Cheng Zhou et al. “Topp-mpc-based dual-arm dynamic collaborative manipulation for multi-object nonprehensile transportation.” 2022 International Conference on Robotics and Automation (ICRA). IEEE, 2022. pp. 999-1005. [cited by applicant]
Seyed Sina Mirrazavi Salehian et al., “A dynamical system approach for catching softly a flying object: Theory and experiment.” IEEE Transactions on Robotics 32.2 (2016): 462-471. [cited by applicant]
Maksim Surov et al. “Case study in non-prehensile manipulation: planning and orbital stabilization of one-directional rollings for the “Butterfly” robot.” 2015 IEEE international conference on Robotics and Automation (I… [cited by applicant]
Filippo Bertoncelli et al., “Linear time-varying MPC for nonprehensile object manipulation with a nonholonomic mobile robot.” 2020 IEEE international conference on robotics and automation (ICRA). IEEE, 2020. pp. 11032-1… [cited by applicant]
Aaron M. Johnson et al., “A hybrid systems model for simple manipulation and self-manipulation systems.” The International Journal of Robotics Research 35.11 (2016): 1354-1392. [cited by applicant]
Feng Zhu et al., “Optimal control of hybrid switched systems: A brief survey.” Discrete Event Dynamic Systems 25 (2015): 345-364. [cited by applicant]
He Li et al., “Hybrid systems differential dynamic programming for whole-body motion planning of legged robots.” IEEE Robotics and Automation Letters 5.4 (2020): 5448-5455. [cited by applicant]
Neel Doshi et al., “Hybrid differential dynamic programming for planar manipulation primitives.” 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020. pp. 6759-6765. [cited by applicant]
Anil V Rao “A survey of numerical methods for optimal control.” Advances in the astronautical Sciences 135.1 (2009): 497-528. [cited by applicant]
Yuval Tassa et al., “Control-limited differential dynamic programming.” 2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2014. pp. 1168-1175. [cited by applicant]
Yunpeng Pan et al., “Probabilistic differential dynamic programming.” Advances in Neural Information Processing Systems 27 (2014). [cited by applicant]
Zhaoming Xie et al., “Differential dynamic programming with nonlinear constraints.” 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017. pp. 695-702. [cited by applicant]
Rohan Budhiraja et al. “Differential dynamic programming for multi-phase rigid contact dynamics.” 2018 IEEE—RAS 18th International Conference on Humanoid Robots (Humanoids). IEEE, 2018. pp. 1-9. [cited by applicant]
Wilson Jallet et al. “Constrained differential dynamic programming: A primal-dual augmented lagrangian approach.” 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022. [cited by applicant]
Wilson Jallet et al. “ProxNLP: a primal-dual augmented Lagrangian solver for nonlinear programming in Robotics and beyond.” arXiv preprint arXiv:2210.02109 (2022). [cited by applicant]
Etienne Pellegrini et al., “A multiple-shooting differential dynamic programming algorithm.” AAS/AIAA Space Flight Mechanics Meeting. vol. 2. 2017. [cited by applicant]
Xiaobo Zheng et al., “Constrained trajectory optimization with flexible final time for autonomous vehicles.” IEEE Transactions on Aerospace and Electronic Systems 58.3 (2021): 1818-1829. [cited by applicant]
Brian Plancher et al., “A performance analysis of parallel differential dynamic programming on a gpu.” Algorithmic Foundations of Robotics XIII: Proceedings of the 13th Workshop on the Algorithmic Foundations of Robotic… [cited by applicant]
R. Goebel et al., “Hybrid dynamical systems,” IEEE control systems magazine, vol. 29, No. 2, pp. 28-93, 2009. [cited by applicant]
Yan-Bin Jia et al., “Batting an in-flight object to the target.” The International Journal of Robotics Research 38.4 (2019): 451-485. [cited by applicant]
Cheng Zhou et al., “Optimal nonprehensile interception strategy for objects in flight.” 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022. [cited by applicant]
The World Intellectual Property Organization (WIPO) International Search Report for PCT/CN2023/118082 Dec. 9, 2023 7 Pages (including translation). [cited by applicant]