If you train robots like dogs, they learn faster

4CD57C33-C5A4-4A74-83F9-2248F6EC949E

Instead of needing a month, it mastered new “tricks” in just days with reinforcement learning.

Treats-for-tricks works for training dogs — and apparently AI robots, too.

That’s the takeaway from a new study out of Johns Hopkins, where researchers have developed a new training system that allowed a robot to quickly learn how to do multi-step tasks in the real world — by mimicking the way canines learn new tricks.

Continue reading… “If you train robots like dogs, they learn faster”

0

This robot taught itself to walk entirely on its own

Google is creating AI-powered robots that navigate without human intervention—a prerequisite to being useful in the real world.

 Within 10 minutes of its birth, a baby fawn is able to stand. Within seven hours, it is able to walk. Between those two milestones, it engages in a highly adorable, highly frenetic flailing of limbs to figure it all out.

That’s the idea behind AI-powered robotics. While autonomous robots, like self-driving cars, are already a familiar concept, autonomously learning robots are still just an aspiration. Existing reinforcement-learning algorithms that allow robots to learn movements through trial and error still rely heavily on human intervention. Every time the robot falls down or walks out of its training environment, it needs someone to pick it up and set it back to the right position.

Now a new study from researchers at Google has made an important advancement toward robots that can learn to navigate without this help. Within a few hours, relying purely on tweaks to current state-of-the-art algorithms, they successfully got a four-legged robot to learn to walk forward and backward, and turn left and right, completely on its own.

Continue reading… “This robot taught itself to walk entirely on its own”

0

Using imitation and reinforcement learning to tackle long-horizon robotic tasks

BC918138-B18A-4558-BC85-DC20BE4DEDE2

Reinforcement learning (RL) is a widely used machine-learning technique that entails training AI agents or robots using a system of reward and punishment. So far, researchers in the field of robotics have primarily applied RL techniques in tasks that are completed over relatively short periods of time, such as moving forward or grasping objects.

A team of researchers at Google and Berkeley AI Research has recently developed a new approach that combines RL with learning by imitation, a process called relay policy learning. This approach, introduced in a paper prepublished on arXiv and presented at the Conference on Robot Learning (CoRL) 2019 in Osaka, can be used to train artificial agents to tackle multi-stage and long-horizon tasks, such as object manipulation tasks that span over longer periods of time.

Continue reading… “Using imitation and reinforcement learning to tackle long-horizon robotic tasks”

0