Robots that can move autonomously and can make intelligent decisions by perceiving their
environments and surrounding objects are known as autonomous mobile robots. Such …

Robots are traditionally bounded by a fixed embodiment during their operational lifetime,
which limits their ability to adapt to their surroundings. Co-optimizing control and …

Complex robotic systems, such as humanoid robot hands, soft robots, and walking robots,
pose a challenging control problem due to their high dimensionality and heavy non …

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 …

Dung beetles can effectively transport dung pallets of various sizes in any direction across
uneven terrain. While this impressive ability can inspire new locomotion and object …

Since Antiquity, humans have tried to understand the functioning of legged locomotion by
adopting multidisciplinary approaches. In recent years, this question has become central in …

One developing approach for robotic control is the use of networks of dynamic neurons
connected with conductance-based synapses, also known as Synthetic Nervous Systems …

Due to the heavy burden on human drivers when remotely controlling hexapod robots in
complex terrain environments, there is a critical need for robot intelligence to assist in …

This paper proposes the application of the Model Predictive Control (MPC) strategy to the
locomotion of a quadrupedal robot endowed with a Central Pattern Generator (CPG) neural …

The cerebellum is a neural structure essential for learning, which is connected via multiple
zones to many different regions of the brain, and is thought to improve human performance …