Rich and Robust Bio-Inspired Locomotion Control for Humanoid Robots

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Atomistic machine learning has revolutionized atomistic modeling. Traditionally, molecular dynamics simulations relied on classical force fields or electronic structure calculations like Density Functional Theory. Classical force fields are fast but less p ...

Planning multicontact motions in a receding horizon fashion requires a value function to guide the planning with respect to the future, e.g., building momentum to traverse large obstacles. Traditionally, the value function is approximated by computing traj ...

Modular robotics link the reliability of a centralised system with the adaptivity of a decentralised system. It is difficult for a robot with a fixed shape to be able to perform many different types of tasks. As the task space grows, the number of functi ...

The rise of robotic body augmentation brings forth new developments that will transform robotics, human-machine interaction, and wearable electronics. Extra robotic limbs, although building upon restorative technologies, bring their own set of challenges i ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

This article summarizes the recent advancements in the design, fabrication, and control of microrobotic devices for the diagnosis and treatment of brain disorders. With a focus on diverse actuation methods, we discuss how advancements in materials science ...

Robots outside of the fenced factories have to deal with continuously changing environment, this requires fast and flexible modes of control. Planning methods or complex learning models can find optimal paths in complex surroundings, but they are computati ...

This paper builds up the skill of impact aware non prehensile manipulation through a hitting motion by allowing the robot arm to come in contact with the environment with parts other than its end effector. Hitting with other joints allows us to manipulate ...

Given the patchy nature of gas plumes and the slow response of conventional gas sensors, the use of mobile robots for Gas Source Localization (GSL) tasks presents significant challenges. These aspects increase the difficulties in obtaining gas measurements ...

In various robotics applications, the selection of function approximation methods greatly influences the feasibility and computational efficiency of algorithms. Tensor Networks (TNs), also referred to as tensor decomposition techniques, present a versatile ...

Background :Sensory reafferents are crucial to correct our posture and movements, both reflexively and in a cognitively driven manner. They are also integral to developing and maintaining a sense of agency for our actions. In cases of compromised reafferen ...

In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signa ...

Biohybrid systems in which robotic lures interact with animals have become compelling tools for probing and identifying the mechanisms underlying collective animal behavior. One key challenge lies in the transfer of social interaction models from simulatio ...

As demonstrated by the Soft Robotics Toolkit Platform, compliant robotics pose an exciting educational opportunity. Underwater robotics using soft undulating fins is an expansive research topic with applications such as exploration of underwater life or re ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Learning a locomotion policy for quadruped robots has traditionally been constrained to a specific robot morphology, mass, and size. The learning process must usually be repeated for every new robot, where hyperparameters and reward function weights must b ...

Harmful chemical compounds are released daily in warehouses, chemical plants and during environmental emergencies. Their uncontrolled dispersion contributes to the pollution of the atmosphere and threatens human and animal lives. When gas leaks occur, the ...

Recently, cutting-edge brain-machine interfaces (BMIs) have revealed the potential of decoders such as recurrent neural networks (RNNs) in predicting attempted handwriting [1] or speech [2], enabling rapid communication recovery after paralysis. However, c ...

This doctoral thesis navigates the complex landscape of motion coordination and formation control within teams of rotary-wing Micro Aerial Vehicles (MAVs). Prompted by the intricate demands of real-world applications such as search and rescue or surveillan ...

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 ...

Rich and Robust Bio-Inspired Locomotion Control for Humanoid Robots

Chattez avec Graph Search

Advancing understanding and practical performance of machine learning interatomic potentials

Online Multicontact Receding Horizon Planning via Value Function Approximation

Memento Mori: Reliable robustness in self-reconfigurable modular robots

Investigating neural resource allocation in the sensorimotor control of extra limbs

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Small-scale robotic devices for medical interventions in the brain

Exact Obstacle Avoidance for Robots in Complex and Dynamic Environments Using Local Modulation

Hitting with Different Joints of a Robotic Manipulator

Sense in Motion with Belief Clustering: Efficient Gas Source Localization with Mobile Robots

Robot Learning using Tensor Networks

Augmenting locomotor perception by remapping tactile foot sensation to the back

Fast adaptation to rule switching using neuronal surprise

Quantifying the biomimicry gap in biohybrid robot-fish pairs

Educational Soft Underwater Robot with an Electromagnetic Actuation

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

ManyQuadrupeds: Learning a Single Locomotion Policy for Diverse Quadruped Robots

Gas Sensing with Rotary-Wing Nano Aerial Vehicles

33.3 MiBMI: A 192/512-Channel 2.46mm² Miniaturized Brain-Machine Interface Chipset Enabling 31-Class Brain-to-Text Conversion Through Distinctive Neural Codes

Distributed Predictive Formation Control of Autonomous Rotary-Wing Micro Aerial Vehicles

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