Robotic manipulators are widely used in modern manufacturing processes. However, their
deployment in unstructured environments remains an open problem. To deal with the …

Most successes in robotic manipulation have been restricted to single-arm robots, which
limits the range of solvable tasks to pick-and-place, insertion, and objects rearrangement. In …

Closed-chain manipulation occurs when several robot arms perform tasks in cooperation. It
is complex to control a dual-arm system because it requires flexible and adaptable operation …

Deep reinforcement learning (DRL) has achieved great success across multiple fields;
however, in the field of robot control, the acquisition of large amounts of motion data from …

Most successes in robotic manipulation have been restricted to single-arm gripper robots,
whose low dexterity limits the range of solvable tasks to pick-and-place, inser-tion, and …

This paper proposes a novel hierarchical reinforcement learning (HRL) framework of
complex manipulation tasks which integrates the human prior knowledge. The framework …

Current reinforcement learning (RL) in robotics often experiences difficulty in generalizing to
new downstream tasks due to the innate task-specific training paradigm. To alleviate it …

In this work, motivated by recent manufacturing trends, we investigate autonomous robotic
assembly. Industrial assembly tasks require contact-rich manipulation skills, which are …

Imitation Learning (IL) is a powerful paradigm to teach robots to perform manipulation tasks
by allowing them to learn from human demonstrations collected via teleoperation, but has …

Multistep tasks, such as block stacking or parts (dis) assembly, are complex for autonomous
robotic manipulation. A robotic system for such tasks would need to hierarchically combine …