Bridging vision and touch: advancing robotic interaction prediction with self-supervised multimodal learning.

Predicting the consequences of the agent's actions on its environment is a pivotal challenge in robotic learning, which plays a key role in developing higher cognitive skills for intelligent robots. While current methods have predominantly relied on vision and motion data to generate the predicted videos, more comprehensive sensory perception is required for complex physical interactions such as contact-rich manipulation or highly dynamic tasks. In this work, we investigate the interdependence between vision and tactile sensation in the scenario of dynamic robotic interaction.

Sensorized Skin With Biomimetic Tactility Features Based on Artificial Cross-Talk of Bimodal Resistive Sensory Inputs.

Tactility in biological organisms is a faculty that relies on a variety of specialized receptors. The bimodal sensorized skin, featured in this study, combines soft resistive composites that attribute the skin with mechano- and thermoreceptive capabilities. Mimicking the position of the different natural receptors in different depths of the skin layers, a multi-layer arrangement of the soft resistive composites is achieved.

Autonomous detection and sorting of litter using deep learning and soft robotic grippers.

Road infrastructure is one of the most vital assets of any country. Keeping the road infrastructure clean and unpolluted is important for ensuring road safety and reducing environmental risk. However, roadside litter picking is an extremely laborious, expensive, monotonous and hazardous task.

Electronic skins and machine learning for intelligent soft robots.

Soft robots have garnered interest for real-world applications because of their intrinsic safety embedded at the material level. These robots use deformable materials capable of shape and behavioral changes and allow conformable physical contact for manipulation. Yet, with the introduction of soft and stretchable materials to robotic systems comes a myriad of challenges for sensor integration, including multimodal sensing capable of stretching, embedment of high-resolution but large-area sensor arrays, and sensor fusion with an increasing volume of data.

Emergence of behavior through morphology: a case study on an octopus inspired manipulator.

The complex motion abilities of the Octopus vulgaris have been an intriguing research topic for biologists and roboticists alike. Various studies have been conducted on the underlying control architectures employed by these high dimensional biological organisms. Researchers have attempted to replicate these architectures on robotic systems.

Soft robot perception using embedded soft sensors and recurrent neural networks.

Recent work has begun to explore the design of biologically inspired soft robots composed of soft, stretchable materials for applications including the handling of delicate materials and safe interaction with humans. However, the solid-state sensors traditionally used in robotics are unable to capture the high-dimensional deformations of soft systems. Embedded soft resistive sensors have the potential to address this challenge.

Learning dynamic models for open loop predictive control of soft robotic manipulators.

The soft capabilities of biological appendages like the arms of Octopus vulgaris and elephants' trunks have inspired roboticists to develop their robotic equivalents. Although there have been considerable efforts to replicate their morphology and behavior patterns, we are still lagging behind in replicating the dexterity and efficiency of these biological systems. This is mostly due to the lack of development and application of dynamic controllers on these robots which could exploit the morphological properties that a soft-bodied manipulator possesses.

Objective evaluation of oral presentation skills using Inertial Measurement Units.

Oral presentation is considered as one of the most sought after skills by companies and professional organizations and program accreditation agencies. However, both learning process and evaluation of this skill are time demanding and complex tasks that need dedication and experience. Furthermore, the role of the instructor is fundamental during the presentation assessment.