Hi All, Good Morning 🌼🌼 Today I want to share something I read about the temple ritual "Ashtabandham" which is done every few years to bond the idol to the pedestal. This ritual is mostly seen only in Kerala temples. "

Ashtabandham" means a mix (bandham) of eight (ashta) ingredients, a natural adhesive mixed with oil to fix if any corrosion forms between the pedestal and the idol through milk, water, curd or honey used during abhishekam. If the idol falls off from the pedestal,
it is considered as inauspicious, hence to prevent such an eventuality, usually once in every 12 years new Ashtabandam is applied and the bonding ceremony is known as Ashtabandakalasham. In Sri Padmanabhaswamy temple, this ritual was done in 1985 during the reign of
Sri Chithira Thirunal at the Thiruvambadi Sri Krishna temple. The preparation of the adhesive mixture takes a long period of 41 days. The eight ingredients which go into the mixture are 1) powdered conch (Shankhu podi) - 6 palam (1 palam = 60 gm), 2)
powdered chenchalyam or gum secreted from country pine - 4 palam, 3) Kadukka - 2 palam, 4) Kolarakku - 1 palam 5) Kozhipparal - 1 palam 6) River sand from Bharathappuzha - 1 palam 7) Amla - 0.5 palam 8) Cotton used for making sacred thread.
All the items except cotton get fine powdered with the cotton mixed only on the final day. The powder is mixed into a lump and becomes paste when little oil is added. After becoming lump, it is hammered by 4 or 5 men alternatively using hammers made of tamarind wood.
The weight of the hammer should be 8 to 10 kilos. The lump is very soft initially and becomes hot during the hammering and finally hard like stone when the hammering is stopped. The cotton is added only when the bonding material has become a hard lump.
Immediately after adding the cotton the material, the mixture is used for bonding the idol. The prep.of the bonding adhesive is done by people from the Brahmin community who are well-accustomed with temple rituals and hammering is done by people from the "Siva dwaja" community 🙏

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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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