Sensing expectation enables simultaneous proprioception and contact detection in an intelligent soft continuum robot.

A high-level perceptual model found in the human brain is essential to guide robotic control when facing perception-intensive interactive tasks. Soft robots with inherent softness may benefit from such mechanisms when interacting with their surroundings. Here, we propose an expected-actual perception-action loop and demonstrate the model on a sensorized soft continuum robot.

Soft Robotics: A Route to Equality, Diversity, and Inclusivity in Robotics.

Robotics is entering our daily lives. The discipline is increasingly crucial in fields such as agriculture, medicine, and rescue operations, impacting our food, health, and planet. At the same time, it is becoming evident that robotic research must embrace and reflect the diversity of human society to address these broad challenges effectively.

Learning-Based Control for Soft Robot-Environment Interaction with Force/Position Tracking Capability.

Soft robotics promises to achieve safe and efficient interactions with the environment by exploiting its inherent compliance and designing control strategies. However, effective control for the soft robot-environment interaction has been a challenging task. The challenges arise from the nonlinearity and complexity of soft robot dynamics, especially in situations where the environment is unknown and uncertainties exist, making it difficult to establish analytical models.

Octopus-inspired sensorized soft arm for environmental interaction.

Octopuses can whip their soft arms with a characteristic "bend propagation" motion to capture prey with sensitive suckers. This relatively simple strategy provides models for robotic grasping, controllable with a small number of inputs, and a highly deformable arm with sensing capabilities. Here, we implemented an electronics-integrated soft octopus arm (E-SOAM) capable of reaching, sensing, grasping, and interacting in a large domain.

Bioinspired soft robots for deep-sea exploration.

The deep ocean, Earth's untouched expanse, presents immense challenges for exploration due to its extreme pressure, temperature, and darkness. Unlike traditional marine robots that require specialized metallic vessels for protection, deep-sea species thrive without such cumbersome pressure-resistant designs. Their pressure-adaptive forms, unique propulsion methods, and advanced senses have inspired innovation in designing lightweight, compact soft machines.

Soft DAgger: Sample-Efficient Imitation Learning for Control of Soft Robots.

This paper presents Soft DAgger, an efficient imitation learning-based approach for training control solutions for soft robots. To demonstrate the effectiveness of the proposed algorithm, we implement it on a two-module soft robotic arm involved in the task of writing letters in 3D space. Soft DAgger uses a dynamic behavioral map of the soft robot, which maps the robot's task space to its actuation space.

What we look for at .

Science Robotics welcomes papers demonstrating technical and scientific advances, with potential for influence beyond robotics.

Five years of .

Looking back at the last 5 years of and looking forward to the next 5.

Learning to stop: a unifying principle for legged locomotion in varying environments.

Evolutionary studies have unequivocally proven the transition of living organisms from water to land. Consequently, it can be deduced that locomotion strategies must have evolved from one environment to the other. However, the mechanism by which this transition happened and its implications on bio-mechanical studies and robotics research have not been explored in detail.

Sharpness recognition based on synergy between bio-inspired nociceptors and tactile mechanoreceptors.

Touch and pain sensations are complementary aspects of daily life that convey crucial information about the environment while also providing protection to our body. Technological advancements in prosthesis design and control mechanisms assist amputees to regain lost function but often they have no meaningful tactile feedback or perception. In the present study, we propose a bio-inspired tactile system with a population of 23 digital afferents: 12 RA-I, 6 SA-I, and 5 nociceptors.

Cerebellar adaptive mechanisms explain the optimal control of saccadic eye movements.

Cerebellar synaptic plasticity is vital for adaptability and fine tuning of goal-directed movements. The perceived sensory errors between desired and actual movement outcomes are commonly considered to induce plasticity in the cerebellar synapses, with an objective to improve desirability of the executed movements. In rapid goal-directed eye movements called saccades, the only available sensory feedback is the direction of reaching error information received only at end of the movement.

Evaluation of the Electroglottographic Signal Variability in Organic and Functional Dysphonia.

Objectives: To confirm the data reported in our previous studies on the analysis of the variability of the electroglottographic signal in the pathological voice; to evaluate possible differences in variability between organic and functional pathologies; to identify any distinctive/typical EGG patterns for these pathologies.

Methods: One hundred twenty-five subjects were enrolled (36 euphonic and 89 pathological: 24 functional dysphonia, 21 bilateral vocal nodules, 23 unilateral polyps and 21 unilateral cysts). All subjects were studied with videolaryngostroboscopy, spectrographic analysis of voice and electroglottography (EGG).

Bioinspired underwater legged robot for seabed exploration with low environmental disturbance.

Robots have the potential to assist and complement humans in the study and exploration of extreme and hostile environments. For example, valuable scientific data have been collected with the aid of propeller-driven autonomous and remotely operated vehicles in underwater operations. However, because of their nature as swimmers, such robots are limited when closer interaction with the environment is required.

A vision for future bioinspired and biohybrid robots.

Bioinspired and biohybrid robots can help respond to diverse, sustainable application needs.

Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience.

Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke.

Locomotory behaviour of the intertidal marble crab (Pachygrapsus marmoratus) supports the underwater spring-loaded inverted pendulum as a fundamental model for punting in animals.

In aquatic pedestrian locomotion the dynamics of terrestrial and aquatic environments are coupled. Here we study terrestrial running and aquatic punting locomotion of the marine-living crab Pachygrapsus marmoratus. We detected both active and passive phases of running and punting through the observation of crab locomotory behaviour in standardized settings and by three-dimensional kinematic analysis of its dynamic gaits using high-speed video cameras.

A Digital Hardware System for Spiking Network of Tactile Afferents.

In the present research, we explore the possibility of utilizing a hardware-based neuromorphic approach to develop a tactile sensory system at the level of first-order afferents, which are slowly adapting type 1 (SA-I) and fast adapting type 1 (FA-I) afferents. Four spiking models are used to mimic neural signals of both SA-I and FA-I primary afferents. Next, a digital circuit is designed for each spiking model for both afferents to be implemented on the field-programmable gate array (FPGA).

A Cerebellum-Inspired Learning Approach for Adaptive and Anticipatory Control.

The cerebellum, which is responsible for motor control and learning, has been suggested to act as a Smith predictor for compensation of time-delays by means of internal forward models. However, insights about how forward model predictions are integrated in the Smith predictor have not yet been unveiled. To fill this gap, a novel bio-inspired modular control architecture that merges a recurrent cerebellar-like loop for adaptive control and a Smith predictor controller is proposed.

Combining Evolutionary and Adaptive Control Strategies for Quadruped Robotic Locomotion.

In traditional robotics, model-based controllers are usually needed in order to bring a robotic plant to the next desired state, but they present critical issues when the dimensionality of the control problem increases and disturbances from the external environment affect the system behavior, in particular during locomotion tasks. It is generally accepted that the motion control of quadruped animals is performed by neural circuits located in the spinal cord that act as a Central Pattern Generator and can generate appropriate locomotion patterns. This is thought to be the result of evolutionary processes that have optimized this network.

Foot Inertial Sensing for Combined Cognitive-Motor Exercise of the Sustained Attention Domain.

Objective: In the past decade, cognitive training tools have been used to improve brain functioning, and some have been applied to cognitive decline in dementia. Recently, research studies have demonstrated that aerobic exercise could play an important restorative role toward cognitive impairments. Therefore, the main objective of this paper is to present the innovative use of the SmartWalk tool, which combines aerobic exercise and cognitive protocols traditionally used to stimulate cognitive function.