Autonomous robots are expected to perform a wide range of sophisticated tasks in complex,
unknown environments. However, available onboard computing capabilities and algorithms …

During human walking, the centre of mass (CoM) is outside the base of support for most of
the time, which poses a challenge to stabilizing the gait pattern. Nevertheless, most of us are …

Many inspirations for soft robotics are from the natural world, such as octopuses, snakes,
and caterpillars. Here, we report a caterpillar-inspired, energy-efficient crawling robot with …

Legged locomotion can extend the operational domain of robots to some of the most
challenging environments on Earth. However, conventional controllers for legged …

Birds take off and land on a wide range of complex surfaces. In contrast, current robots are
limited in their ability to dynamically grasp irregular objects. Leveraging recent findings on …

Exoskeletons and active prostheses promise to enhance human mobility, but few have
succeeded. Optimizing device characteristics on the basis of measured human performance …

Deep reinforcement learning (DRL) has proven successful for many difficult control
problems by learning policies represented by neural networks. However, the complexity of …

While deep reinforcement learning has successfully solved many challenging control tasks,
its real-world applicability has been limited by the inability to ensure the safety of learned …

The limitations and complexity of traditional noncontact sensors in terms of sensitivity and
threshold settings pose great challenges to extend the traditional five human senses. Here …

Physics-based predictive simulations of human movement have the potential to support
personalized medicine, but large computational costs and difficulties to model control …