看看这个用强化学习练水瓶的机器人，结果连零食罐和软袋也能捡，效率还涨了40%
研究人员用强化学习训练机器人从杂乱容器中捡起水瓶并递给人类，仅针对水瓶训练。测试显示，机器人在水瓶上的成功率从80%提升至98%，对未训练的零食罐吞吐量提高40%，对更难处理的软袋性能提高13%。这表明强化学习泛化能力可以节省机器人训练成本。
These guys trained a robot with water bottles, and…
These guys trained a robot with water bottles, and the robot learned to pick up other objects it had never practiced on.
They did this using reinforcement learning:
1. The robot had to pick a bottle out of a messy tote 2. Then it had to hand the bottle to a person 3. Only water bottles. Nothing else.
After they finished training, they tested the robot on different objects:
• On bottles, the success went from 80% to 98% • On snack cans, the robot's throughput rose 40% • On soft bags, the robot's performance improved by 13% even though they're deformable and much harder to handle than rigid objects.
Snack cans and soft bags have different shapes and weights, and the robot was never trained on them.
This was just one of three use cases they tested.
Every hour we spend teaching a robot costs a ton of money. If they only get better at handling the one object they practice on, reinforcement learning will never be worth doing.
But if the gains carry over, we could focus on teaching the hardest possible tasks and let the robot apply that knowledge elsewhere.
Here is the full write-up by @TheHumanoidAI on the infrastructure they used and what they learned:
https://t.co/bslHUyjAGy
#ad