Humanoids 🤖 will do anything humans can do. But are state-of-the-art algorithms up to the challenge?
Introducing HumanoidBench, the first-of-its-kind simulated humanoid benchmark with 27 distinct whole-body tasks requiring intricate long-horizon planning and coordination.
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Access to *diverse* training data is a major bottleneck in robot learning. We're releasing DROID, a large-scale in-the-wild manipulation dataset. 76k trajectories, 500+ scenes, multi-view stereo, language annotations etc
Check it out & download today!
💻: droid-dataset.github.io
Looking for a challenging manipulation benchmark?
Introducing FurnitureBench 🪑🛠️, a reproducible real-world furniture assembly benchmark (#RSS2023@RoboticsSciSys)!
It features
* 8 furniture models
* 200+ hours of expert demonstrations
* FurnitureSim: Isaac Gym simulator
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Having humans annotate data to pre-train robots is expensive and time-consuming!
Introducing SPRINT:
A pre-training approach using LLMs and offline RL to equip robots w/ many language-annotated skills while minimizing human annotation effort!
URL: clvrai.github.io/sprint/
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133 Followers 317 FollowingM.S. in Robotics at CMU with @shubhtuls
Prev Research Intern at VC Lab advised by @jhugestar
B.S. Meche & Math at Seoul National University
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Please everyone be sure to use the robot in a Friendly and Safe manner.
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