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Homanga Bharadhwaj

@mangahomanga • 3,024 subscribers

AI Research Scientist @Meta Reality Labs | Hater of hot takes. Lover of analyses.

Shorts

Checkout ObjectForesight! We tackle the problem of predicting future 3D object trajectories given a past context, purely from human videos of everyday interactions! [1/n]

Checkout ObjectForesight! We tackle the problem of predicting future 3D object trajectories given a past context, purely from human videos of everyday interactions! [1/n]

13,955 次观看

RGB Human Videos -> Dexterous Manipulation Policies No teleoperation. No wearables. No robot interaction. The 🔑: Reliable 3D hand-object trajectory reconstruction and efficient action augmentation Fun collaboration led by Hongyi Chen

RGB Human Videos -> Dexterous Manipulation Policies No teleoperation. No wearables. No robot interaction. The 🔑: Reliable 3D hand-object trajectory reconstruction and efficient action augmentation Fun collaboration led by Hongyi Chen

10,761 次观看

Converting video models ➡️ ego-centric world models capable of humanoids manipulating objects, robots navigating through paintings, and beyond! Huge effort led by Anurag Bagchi over a year! Special thanks to 1X for releasing so much data!

Converting video models ➡️ ego-centric world models capable of humanoids manipulating objects, robots navigating through paintings, and beyond! Huge effort led by Anurag Bagchi over a year! Special thanks to 1X for releasing so much data!

10,478 次观看

Track2Act: Our latest on training goal-conditioned policies for diverse manipulation in the real-world. We train a model for embodiment-agnostic point track prediction from web videos combined with embodiment-specific residual policy learning 1/n

Track2Act: Our latest on training goal-conditioned policies for diverse manipulation in the real-world. We train a model for embodiment-agnostic point track prediction from web videos combined with embodiment-specific residual policy learning 1/n

34,971 次观看

Videos

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No teleoperation. No simulation. No RL. Multi-fingered robot manipulation policies directly by watching videos of humans with Aria glasses on. It was super fun working on this with Irmak Guzey's lead!! 1/n

No teleoperation. No simulation. No RL. Multi-fingered robot manipulation policies directly by watching videos of humans with Aria glasses on. It was super fun working on this with Irmak Guzey's lead!! 1/n

Homanga Bharadhwaj

50,758 次观看 • 6 个月前

Gen2Act: Casting language-conditioned manipulation as *human video generation* followed by *closed-loop policy execution conditioned on the generated video* enables solving diverse real-world tasks unseen in the robot dataset! 1/n

Gen2Act: Casting language-conditioned manipulation as human video generation followed by closed-loop policy execution conditioned on the generated video enables solving diverse real-world tasks unseen in the robot dataset! 1/n

Homanga Bharadhwaj @ CVPR

71,132 次观看 • 1 年前

Presenting DemoDiffusion: An extremely simple approach enabling a pre-trained 'generalist' diffusion policy to follow a human-demonstration for a novel task during inference One-shot human imitation *without* requiring any paired human-robot data or online RL 🙂 1/n

Presenting DemoDiffusion: An extremely simple approach enabling a pre-trained 'generalist' diffusion policy to follow a human-demonstration for a novel task during inference One-shot human imitation without requiring any paired human-robot data or online RL 🙂 1/n

Homanga Bharadhwaj

32,830 次观看 • 11 个月前

Humans grasp objects with a purpose! Web2Grasp enables such functional grasping for dexterous robot hands via hand-object reconstruction from web images - without *any* robot teleop data collection 1/n

Humans grasp objects with a purpose! Web2Grasp enables such functional grasping for dexterous robot hands via hand-object reconstruction from web images - without any robot teleop data collection 1/n

Homanga Bharadhwaj @ CVPR

28,408 次观看 • 1 年前

Motion cues from human videos + Reasoning from VLMs enables 3D hand trajectory prediction in-the-wild for novel tasks in novel scenes! Very excited to share the EgoMan project, a huge effort led by Mingfei Chen during an internship with us at Meta. Video summary ⬇️

Motion cues from human videos + Reasoning from VLMs enables 3D hand trajectory prediction in-the-wild for novel tasks in novel scenes! Very excited to share the EgoMan project, a huge effort led by Mingfei Chen during an internship with us at Meta. Video summary ⬇️

Homanga Bharadhwaj @ CVPR

10,506 次观看 • 5 个月前

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