🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

Over the past few months we've been working with Boston Dynamics on end-to-end dexterous manipulation for the EAtlas:

So cool to see end-to-end dexterous grasping being applied to humanoids

Super impressive pipeline!

Exiciting to see (at 5:55) Nvidia adopting LEAP Hand in their sim2real efforts! Build your own at leaphand.com ! Lots more coming this summer, stay tuned :) Deepak Pathak Ananye Agarwal

Robot data is expensive and hard to scale But what if we could collect rich, diverse demos—with just our hands? 🙌 Our latest work, DexWild, shows how large-scale human data 💪 + robot data 🦾 co-training enables strong generalization across tasks, scenes, and embodiments

Introducing DexWild -- a scalable approach to diverse "in the wild" data collection for dexterous robotic hands! This data can be used to co-train policy for any downstream robotic hands on any body form factor (humanoids, AMR with arms, etc). 🚀🤖

Very exciting Handy Moves workshop at ICRA 2025 this year! It's an honor to be hosting this morning session! Please join us in Room 302 😀 sites.google.com/view/dexterity…

Excited to share our work: Maximizing Confidence Alone Improves Reasoning Humans rely on confidence to learn when answer keys aren’t available (e.g taking an exam). Surprisingly, LLMs can also learn w/o ground-truth answers, simply by reinforcing high-confidence answers via RL!

One fundamental issue with RL – whether it’s for robots or LLMs – is how hard it is to get rewards. For LLM reasoning, we need ground-truth labels to verify answers. We found that maximizing confidence alone allows LLMs to improve their reasoning with RL!

Tidying should feel robotic 🔊🐝

RL with verifiable reward has shown impressive results in improving LLM reasoning, but what can we do when we do not have ground truth answers? Introducing Self-Rewarding Training (SRT): where language models provide their own reward for RL training! 🧵 1/n

Maximizing Confidence Alone Improves Reasoning Feels like the start of the "curiosity-driven learning" era for LLMs. I have spent most of my career towards building agents that can self-improve without any external rewards (e.g., curiosity work during Phd and then at CMU).

A legged mobile manipulator trained to play badminton with humans coordinates whole-body maneuvers and onboard perception. Paper: science.org/doi/10.1126/sc……Video: youtu.be/zYuxOVQXVt8 Yuntao Ma, Andrei Cramariuc, Farbod Farshidian, Marco Hutter

🧑🤖 Introducing Human2Sim2Robot!  💪🦾 Learn robust dexterous manipulation policies from just one human RGB-D video. Our Real→Sim→Real framework crosses the human-robot embodiment gap using RL in simulation. #Robotics #DexterousManipulation #Sim2Real 🧵1/7

🤖Can a humanoid robot carry a full cup of beer without spilling while walking 🍺? Hold My Beer ! Introducing Hold My Beer🍺: Learning Gentle Humanoid Locomotion and End-Effector Stabilization Control Project: lecar-lab.github.io/SoFTA/ See more details below👇

Introducing Mobi-π: Mobilizing Your Robot Learning Policy. Our method: ✈️ enables flexible mobile skill chaining 🪶 without requiring additional policy training data 🏠 while scaling to unseen scenes 🧵↓

Your bimanual manipulators might need a Robot Neck 🤖🦒 Introducing Vision in Action: Learning Active Perception from Human Demonstrations ViA learns task-specific, active perceptual strategies—such as searching, tracking, and focusing—directly from human demos, enabling robust