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Tip from the Monkey
Pangolins, September 2019 and PLA are the key to this mystery
Stay Tuned!


1. Yang


2. A jacobin capuchin dangling a flagellin pangolin on a javelin while playing a mandolin and strangling a mannequin on a paladin's palanquin, said Saladin
More to come tomorrow!


3. Yigang Tong
https://t.co/CYtqYorhzH
Archived: https://t.co/ncz5ruwE2W


4. YT Interview
Some bats & pangolins carry viruses related with SARS-CoV-2, found in SE Asia and in Yunnan, & the pangolins carrying SARS-CoV-2 related viruses were smuggled from SE Asia, so there is a possibility that SARS-CoV-2 were coming from
How can we use language supervision to learn better visual representations for robotics?

Introducing Voltron: Language-Driven Representation Learning for Robotics!

Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z

🧵👇(1 / 12)


Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.

Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)

The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (
https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).

The secret is *balance* (3/12)

Starting with a masked autoencoder over frames from these video clips, make a choice:

1) Condition on language and improve our ability to reconstruct the scene.

2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)

By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.

Why is the ability to shape this balance important? (5/12)

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