Li, Zhibin

Komura, Taku

Hu, Wenbin

2023-09-19T10:19:11Z

2023-09-19T10:19:11Z

2023-09-19

Endowing robots with human-level grasping and manipulation skills is an appealing yet challenging research topic over decades. Towards more extensive functionalities, the robots should interact with the objects and environment in a more robust, efficient and intelligent way. With the rapid development of deep learning, merging the learning-based algorithms into the perception and control loop of the robot exhibits promising performances. In this thesis, we explore the implementation of model-free deep reinforcement learning (DRL) and learning from demonstrations (LfD) in acquisition of dynamic grasping and manipulation motor skills of a robotic hand-arm system. We propose a systematic framework for end-to-end learning robotic control policies with model-free DRL, including simulation set-up, reward design and sim-to-real transfer. The DRL-based training framework is evaluated by three case studies with different robotic tasks and research emphases. We first introduce the framework in Chapter 3, with a focus on the design of reward function and special initial training states. The trained policy coordinately controls the robotic hand and arm for dynamic reaching, grasping and re-grasping of objects sliding on the ground. We further propose a multi-modular structure consisting of three control policies trained with proposed framework (Chapter 5). With seamless cross-module integration achieved by the gating policy network, the robot can catch in-flight objects with mitigated impact forces. In Chapter 6, dexterous in-hand manipulation skills with tactile feedback is trained from scratch in simulation and directly transferred to real robot. We focus on the exploitation of tactile perception and sim-to-real transfer methods. Moreover, in Chapter 4 we propose a low-cost method to collect human demonstration data for supervised learning. Using only proprioceptive sensing, the trained neural network based controllers can grasp property-unknown objects with adaptive grasping forces.

https://hdl.handle.net/1842/40924

http://dx.doi.org/10.7488/era/3676

en

The University of Edinburgh

Wenbin Hu, Chuanyu Yang, Kai Yuan, Zhibin Li. “Learning Motor Skills of Reactive Reaching and Grasping of Objects” IEEE International Conference on Robotics and Biomimetics (ROBIO), 2021.

Shuaijun Wang, Wenbin Hu, Lining Sun, Xin Wang, Zhibin Li. “Learning Adaptive Grasping from Human Demonstrations” IEEE/ASME Transactions on Mechatronics, 202

grasping

manipulation

deep reinforcement learning

learning from demonstrations

tactile feedback

Learning robotic motor skills for dynamic grasping, catching, and dexterous in-hand manipulation

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy