Soft robots' flexibility and compliance give them the potential to outperform traditional rigid-
bodied robots while performing multiple tasks in unexpectedly changing environments and …

Soft robotics is a rapidly evolving field where robots are fabricated using highly deformable
materials and usually follow a bioinspired design. Their high dexterity and safety make them …

Embedding polygonal mesh assets within photorealistic Neural Radience Fields (NeRF)
volumes, such that they can be rendered and their dynamics simulated in a physically …

Abstract Learning from Demonstration (LfD) is an efficient technique for robots to acquire
new skills through expert observation, significantly mitigating the need for laborious manual …

Reconstructing and simulating elastic objects from visual observations is crucial for
applications in computer vision and robotics. Existing methods, such as 3D Gaussians …

Soft actuators with integrated sensing have shown utility in a variety of applications such as
assistive wearables, robotics, and interactive input devices. Despite their promise, these …

Deep reinforcement learning can generate complex control policies, but requires large
amounts of training data to work effectively. Recent work has attempted to address this issue …

We propose a hybrid neural network (NN) and PDE approach for learning generalizable
PDE dynamics from motion observations. Many NN approaches learn an end-to-end model …

The current dominant paradigm for robotic manipulation involves two separate stages:
manipulator design and control. Because the robot's morphology and how it can be …

Cloth simulation has wide applications in computer animation, garment design, and robot-
assisted dressing. This work presents a differentiable cloth simulator whose additional …