Advances in material sciences, control algorithms, and manufacturing techniques have
facilitated rapid progress in soft grippers, propelling their adoption in various fields. In this …

We introduce Rotateit, a system that enables fingertip-based object rotation along multiple
axes by leveraging multimodal sensory inputs. Our system is trained in simulation, where it …

In-hand object reorientation is necessary for performing many dexterous manipulation tasks,
such as tool use in less structured environments, which remain beyond the reach of current …

Recent work has demonstrated the ability of deep reinforcement learning (RL) algorithms to
learn complex robotic behaviours in simulation, including in the domain of multi-fingered …

Generalized in-hand manipulation has long been an unsolved challenge of robotics. As a
small step towards this grand goal, we demonstrate how to design and learn a simple …

Dexterous manipulation has been a long-standing challenge in robotics. While machine
learning techniques have shown some promise, results have largely been currently limited …

Tactile information plays a critical role in human dexterity. It reveals useful contact
information that may not be inferred directly from vision. In fact, humans can even perform in …

Executing contact-rich manipulation tasks necessitates the fusion of tactile and visual
feedback. However, the distinct nature of these modalities poses significant challenges. In …

In-hand object reorientation is necessary for performing many dexterous manipulation tasks,
such as tool use in unstructured environments that remain beyond the reach of current …

Humans excel at dexterous manipulation; however, achieving human-level dexterity
remains a significant challenge for robots. Technological breakthroughs in the design of …