Papers accepted at ICRA@40
Two papers and a journal accepted at the 40th Anniversary of the IEEE Conference on Robotics & Automation (ICRA@40), held in Rotterdam
Joao Moura, Theodoros Stouraitis, Namiko Saito and Sethu Vijayakumar, Optimal Shared Autonomy for Contact-rich Robotic Manipulation, Proc. 40th Anniversary of the IEEE Conference on Robotics and Automation (ICRA@40), Rotterdam, Netherlands (2024). [pdf] [citation]
This extended abstract proposes a conceptual framework for combining human user/operator input with autonomous reasoning for remote handling of contact-rich manipulation tasks, outlined in Fig. 1. We propose an optimal control (OC) paradigm that incorporates models from hybrid contact dynamics, compliant interaction, and operator intention as a means of expanding current robotic manipulation capabilities whilst ensuring safe and stable task execution. Through our formalism, we outline technical and scientific challenges of remote handling of contact-rich manipulation tasks and identify opportunities for novel research directions.
Namiko Saito, Joao Moura and Sethu Vijayakumar, Long-horizon Manipulation through Hierarchical Motion Planning with Subgoal Prediction, Proc. 40th Anniversary of the IEEE Conference on Robotics and Automation (ICRA@40), Rotterdam, Netherlands (2024). [pdf] [video] [citation]
The research on long-horizon manipulation in environments with numerous objects and subtasks falls under the framework of task and motion planning (TAMP). One effective solution for TAMP is to separate higher-level discrete short-horizon subgoals, and lower-level continuous motion generation to enhance robustness, scalability, and generalizability. We propose a concept of hierarchical framework combining deep neural networks (DNN) for higher-level subgoal decisions and optimization for lower-level motion control. This will be evaluated on a latent state box transport and stacking task – where the robot needs to change the order of actions and speed to control during motion execution. Additionally, we can apply this framework to daily tasks such as cooking, where the robot needs to recognise the states of ingredients, select appropriate tools and subtasks, and adjust its motions accordingly.
Lei Yan, Theodoros Stouraitis, Joao Moura, Wenfu Xu, Michael Gienger, and Sethu Vijayakumar, Impact-Aware Bimanual Catching of Large-Momentum Objects, IEEE Transactions on Robotics (T-RO), vol. 40, pp. 2543-2563 (2024) (Presented at: 40th Anniversary of IEEE International Conference on Robotics and Automation (ICRA@40), Rotterdam, Netherlands) [pdf] [DOI] [video] [citation]
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.