Reinforcement learning techniques could be the keys to integrating robots — who use machine learning to output more than words — into the real world.

Therefore, the present work proposes automatic calibration for color segmentation and robot detection systems as a black-box optimization problem. Then, applying the formulation of the IEEE VSSS robot ...

For example, a pair of robot legs called Cassie taught itself to walk using reinforcement learning, but only after it had done so in a simulation. “The problem is your simulator will never be as ...

Repurposing Reinforcement Learning for Task-to-Task Transfer; and “Good Robot!”: Efficient Reinforcement Learning for Multi-Step Visual Tasks with Sim to Real Transfer ... If you are using other ...

Our approach models the calibration process compactly using model-free deep reinforcement learning to derive a policy that guides the motions of a robotic arm holding the sensor to efficiently collect ...

Credit: He et al "H 2 O teleoperation is a framework based on reinforcement learning (RL) that facilitates the real-time whole-body teleoperation of humanoid robots using just an RGB camera," He ...

A reinforcement learning formulation created with RoboMaker and SageMaker uses joint states and camera views as inputs to a model that outputs optimal trajectories for manipulating the valves.

This agile robot dog uses a video camera in place of senses. ... and even hopping over small gaps while relying only an onboard visual camera and some AI reinforcement learning.

Therefore, the present work proposes automatic calibration for color segmentation and robot detection systems as a black-box optimization problem. Then, applying the formulation of the IEEE VSSS robot ...