Axis Robotics India (@axisroboticsin_)

Axis Robotics India @axisroboticsin_ 10h

Ab har trajectory par base:0x5be75e3b0aff5d1a042a5363ae36acb1d6d755ea earn hoga.

Points ka system ab khatam ho chuka hai. Saturday, 14 November se har verified aur signed trajectory par aapko [$AXIS] milega, aur aap har hafte apne rewards claim kar sakte hain.

Abhi explore karein: hub.axisrobotics.ai

Yeh kaise kaam karta hai, neeche dekhein.

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Axis Robotics @axisrobotics 12h

Every trajectory now earns $AXIS.

Points are done. From Saturday 14 November, every verified, signed trajectory earns $AXIS, and you can claim it every week.

Explore now: hub.axisrobotics.ai

How it works ⬇️

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Axis Robotics India @axisroboticsin_ 4d

Axis Sim Training Live Kal Ho Raha Hai

Kya aap Axis Robotics simulation training ko practically seekhne ke liye taiyar hain?

@drag4_t ek live training session host karega with co-host @Retrix007 ke sath, jahan Biswa poore training process ko step by step samjheyega.

Session mein hum kya cover karenge:

→ Training tasks ko kaise access karein
→ Robot simulation ko samajhna
→ Robot movement controls
→ Wheeled Franka Tasks Ko Kaise Complete Karein
→ Trajectories kaise submit karein
→ Common errors aur troubleshooting
→ New task availability, supply aur slots
→ Live Q&A

Session Details:

📅 Thursday, 12 November 2026
⏰ 8:00 PM IST
📍 Location: Discord (Indian Voice Channel)
🎤 Host: @drag4_t
🤝 Co-Host: @Retrix007
⏳ Duration: 45 Minutes

Chahe aap beginner hon ya pehle se contribution kar rahe hon, yeh session aapko workflow samajhne aur apne training experience ko improve karne mein madad karega.

Is session ko miss mat karna. Aaiye, saath mein seekhte hain, training karte hain aur build karte hain.

Agar aap avi tak humhara Discord Join nahi kiye ho to kar lijiye : discord.gg/jYWskxESrJ

Axis Robotics India @axisroboticsin_ 5d

Hum visual policy distillation ke liye ODE Policy ka use kar rahe hain aur abhi tak ka initial result kaafi achha raha hai. Actions samay ke saath kaise evolve hote hain, yeh seekhne se policy ko robot movements ki continuity ko naturally capture karne ka ek behtar tareeka milta hai.

Video mein hamara Franka robot simulation mein ek nut ko bolt par chadhana seekh raha hai aur SGS se is task ko reproduce kar raha hai.

Aage hum ODE Policy ka use karke assembly skills ki ek badi range ko policies mein distill karne ki planning kar rahe hain, jo camera images ke basis par kaam karengi. Ismein gears ko fit karna, pegs insert karna aur connectors ko jodna jaise tasks shamil hain.

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Axis Robotics @axisrobotics 5d

We’ve been using ODE Policy for visual policy distillation, with strong early results. Learning how actions evolve over time gives the policy a natural way to capture the continuity of robot motion.

In the video, our Franka robot learns to thread a nut onto a bolt in simulation, reproducing the task from SGS.

Next, we plan to use ODE Policy to distill a wider range of assembly skills into policies that act from camera images, from fitting gears and inserting pegs to joining connectors.

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Axis Robotics India @axisroboticsin_ 5d

Jab real robot data limited ho, toh co-training ke liye simulation demonstrations ka selection kaise karna chahiye?

Pesh hai TUCO: Curating Simulation Demonstrations for Sim-to-Real Robot Policy Co-Training, jo sim-to-real robot policy co-training ke liye data curation par pehli systematic study hai.

Sirf har task ke liye 10 real demonstrations aur 600 carefully curated simulation demonstrations ka use karke TUCO ne 85.8% real-robot success rate achieve ki.

Iske comparison mein, poore 1,200 demonstrations pool ya randomly selected 600 demonstrations ke saath success rate sirf 25.8% rahi.

TUCO ne teeno transfer settings mein SOTA (State-of-the-Art) performance bhi achieve ki.

Yeh research in universities ke researchers ke saath milkar ki gayi hai:
@Stanford
@NTUsg
@CarnegieMellon
@FudanUniversity
@UCBerkeley
@sjtu1896

Project Page: tuco-curation.github.io

Complete details neeche dekhein.

2:46

Axis Robotics @axisrobotics 6d

How should you choose simulation demos for co-training when real robot data is scarce?

Introducing TUCO: Curating Simulation Demonstrations for Sim-to-Real Robot Policy Co-Training, the first systematic study of data curation for sim-to-real robot policy co-training.

With just 10 real demos and 600 curated sim demos per task, TUCO hits 85.8% real-robot success, versus 25.8% for the full 1,200 pool or a random 600, and sets SOTA across all three transfer settings.

A collaboration with researchers from @Stanford @NTUsg @CarnegieMellon @FudanUniversity @UCBerkeley @sjtu1896.

Project page: tuco-curation.github.io

Details below ⬇️

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Axis Robotics India @axisroboticsin_ 5d

Hum ek multi-layered robotics architecture build kar rahe hain, jahan LLM high-level reasoning aur skill harness ko control karta hai.

Yeh raha iska ek demo.

Task: Right arm ka use karke tray mein rakhi teen objects ko table ke X-axis ke along ek seedhi line mein arrange karna hai. Objects ko unki height ke according tallest se shortest order mein rakhna hai.

Aap dekh sakte hain ki agent kaise is task ko continuous movements mein convert karta hai. Skill harness ki madad se scene understanding, height ke according objects ki ordering, placement planning aur physical execution ko ek saath connect kiya jaata hai.

Yeh robotics ke future ki taraf ek bahut hi exciting shuruaat hai.

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Axis Robotics @axisrobotics Nov 6

We're building a multi-layered robotics architecture, where the LLM drives high-level reasoning and skill harness.

Here's a demo. The task: use the right arm to arrange three objects from the tray in a row along the X axis of the table, ordered by height from tallest to shortest.

You can see the agent turn the task into continuous motion through skill harness, linking scene understanding, height-based ordering, and placement planning to physical execution.

This is an exciting start.

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Axis Robotics India

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