Impact-Aware Bimanual Catching of Large-Momentum Objects

Lei Yan, Theodoros Stouraitis, Joao Moura, Wenfu Xu*, Michael Gienger, Sethu Vijayakumar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

This paper investigates one of the most challenging tasks in dynamic manipulation—catching large-momentum moving objects. Beyond the realm of quasi-static manipulation, dealing with highly dynamic objects can significantly improve the robot’s capability of interacting with its surrounding environment. Yet, the inevitable motion mismatch between the fast moving object and the approaching robot will result in large impulsive forces, which lead to the unstable contacts and irreversible damage to both the object and the robot. To address the above problems, we propose an online optimization framework to: 1) estimate and predict the linear and angular motion of the object; 2) search and select the optimal contact locations across every surface of the object to mitigate impact through sequential quadratic programming (SQP); 3) simultaneously optimize the end-effector motion, stiffness, and contact force for both robots using multi-mode trajectory optimization (MMTO); and 4) realise the impact-aware catching motion on the compliant robotic system based on indirect force controller. We validate the impulse distribution, contact selection, and impactaware MMTO algorithms in simulation and demonstrate the benefits of the proposed framework in real-world experiments including catching large-momentum moving objects with welldefined motion, constrained motion and free-flying motion.
Original languageEnglish
Pages (from-to)1-20
Number of pages20
JournalIEEE Transactions on Robotics
Early online date25 Mar 2024
Publication statusE-pub ahead of print - 25 Mar 2024

Keywords / Materials (for Non-textual outputs)

  • impact
  • large-momentum object
  • dynamic manipulation
  • bimanual catching
  • trajectory optimization


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