why? how is having a way more complex way of manipulating the door handle gonna make the whole thing more robust? it will get more possible points of failure than before and improve nothing. what could help is a better way of sensing where the door handle is. maybe using an end-effector-mounted camera.
I wonder if it's easy or hard to combine this style of in house model training with the ability to generalize of large multimodal models. This looks very robust and efficient for specific actions, and the large model can act as a decision making director that can choose which specific actions to take. For example, if the door is locked, i should look for another door to the same room and try that one, or look for an alternative route. Or if the terrain is detected to be too hard to traverse, look for another path, or even try to remove rubble and objects that are blocking the path first, or wait for the green light to cross a road.
I'm very curious to see if the modern AI/software solutions can be tapped into the existing failing robot (the exact robot that fails in the competition long ago)....And see if it can respond successfully.
![Identifying Terrain Physical Parameters from Vision [RA-L]](http://i.ytimg.com/vi/SrAE0IK6UnE/mqdefault.jpg)
![Identifying Terrain Physical Parameters from Vision [RA-L]](/img/tr.png)
Can't hide from them behind the door anymore
It is so satisfying to watch!! What a great job
Very impressive! It’s great progress towards real automation.
Incredible video and great work 👏
Amazing work. If there will be a gripper with this robot, this must perform the task much more robust.
why? how is having a way more complex way of manipulating the door handle gonna make the whole thing more robust? it will get more possible points of failure than before and improve nothing.
what could help is a better way of sensing where the door handle is. maybe using an end-effector-mounted camera.
nice step towards real autonomy :D
Good explanation and honest evaluation :)
I wonder if it's easy or hard to combine this style of in house model training with the ability to generalize of large multimodal models. This looks very robust and efficient for specific actions, and the large model can act as a decision making director that can choose which specific actions to take. For example, if the door is locked, i should look for another door to the same room and try that one, or look for an alternative route. Or if the terrain is detected to be too hard to traverse, look for another path, or even try to remove rubble and objects that are blocking the path first, or wait for the green light to cross a road.
Very impressive. Is the model computed locally on the device?
Amazing video!
I'm very curious to see if the modern AI/software solutions can be tapped into the existing failing robot (the exact robot that fails in the competition long ago)....And see if it can respond successfully.