In recent years, quadruped mobile robots' development had experienced rapid progress,
which has subsequently increased their usage in many human-assisted applications …

Manual assessments of plant phenotypes in the field can be labor-intensive and inefficient.
The high-throughput field phenoty** systems and in particular robotic systems play an …

Simulation-based reinforcement learning approaches are leading the next innovations in
legged robot control. However, the resulting control policies are still not applicable on soft …

Agile maneuvers such as sprinting and high-speed turning in the wild are challenging for
legged robots. We present an end-to-end learned controller that achieves record agility for …

The interest in exploring planetary bodies for scientific investigation and in situ resource
utilization is ever-rising. Yet, many sites of interest are inaccessible to state-of-the-art …

Legged robots that can operate autonomously in remote and hazardous environments will
greatly increase opportunities for exploration into underexplored areas. Exteroceptive …

In this letter, we propose a locomotion training framework where a control policy and a state
estimator are trained concurrently. The framework consists of a policy network which outputs …

Quadrupedal robots resemble the physical ability of legged animals to walk through
unstructured terrains. However, designing a controller for quadrupedal robots poses a …

Robotic locomotion in unstructured terrain demands an agile, adaptive, and energy-efficient
architecture. To traverse such terrains, legged robots use rigid electromagnetic motors and …

Deep reinforcement learning is a promising approach to learning policies in uncontrolled
environments that do not require domain knowledge. Unfortunately, due to sample …