Recurrent Neural Networks (RNNs) have demonstrated their outstanding ability in sequence
tasks and have achieved state-of-the-art in wide range of applications, such as industrial …

This paper reviews the robotic manipulation of deformable objects in caregiving scenarios.
Deformable objects like clothing, food, and medical supplies are ubiquitous in care tasks, yet …

The automatic design of soft robots characterizes as jointly optimizing structure and control.
As reinforcement learning is gradually used to optimize control, the time-consuming …

Modular robots are made up of a set of components which can be configured and
reconfigured to form customized robots for a wide range of tasks. Fully utilizing the flexibility …

Modular robots hold the promise of changing their shape and even dimension to adapt to
various tasks and environments. To realize this superiority, it is essential to find the …

A popular paradigm in robotic learning is to train a policy from scratch for every new robot.
This is not only inefficient but also often impractical for complex robots. In this work, we …

Robots have been used in all sorts of automation, and yet the design of robots remains
mainly a manual task. We seek to provide design tools to automate the design of robots …

Abstract One degrees-of-freedom (1DOF) linkages are persistent in mechanical systems.
However, designing linkages to follow a desired path, known as path synthesis, is …

We propose a unified approach for automatically generating multiple robot-model
descriptions for modular robot manipulators. Modular robots need models for kinematics and …

With the increased demand for customisation, developing task-specific robots for industrial
and personal applications has become essential. Collaborative robots are often preferred …