Jason Liu
@JasonJZLiu
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PhD Student @CMU_Robotics | Prev: Robot Learning @NvidiaAI | Engineering Science @UofT
USA
Joined September 2014
Low-cost teleop systems have democratized robot data collection, but they lack any force feedback, making it challenging to teleoperate contact-rich tasks. Many robot arms provide force information — a critical yet underutilized modality in robot learning. We introduce:.1. 🦾A
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RT @robot_trainer: The Dex team at NVIDIA is defining the bleeding edge of sim2real dexterity. Take a look below 🧵 There's a lot happening….
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RT @_tonytao_: Want to add diverse, high-quality data to your robot policy?. Happy to share that the DexWild Dataset is now fully public, h….
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RT @TheHumanoidHub: Got to visit the Robotics Institute at CMU today. The institute has a long legacy of pioneering research and pushing t….
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RT @mihirp98: 1/ Maximizing confidence indeed improves reasoning. We worked with @ShashwatGoel7, @nikhilchandak29 @AmyPrb for the past 3 we….
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Neat idea!.
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
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RT @kevin_zakka: We’re super thrilled to have received the Outstanding Demo Paper Award for MuJoCo Playground at RSS 2025!.Huge thanks to e….
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RT @jianglong_ye: How to generate billion-scale manipulation demonstrations easily? Let us leverage generative models! 🤖✨. We introduce Dex….
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Presenting FACTR today at #RSS2025 in the Imitation Learning I session at 5:30pm (June 22). Come by if you're interested in force-feedback teleop and policy learning!.
Low-cost teleop systems have democratized robot data collection, but they lack any force feedback, making it challenging to teleoperate contact-rich tasks. Many robot arms provide force information — a critical yet underutilized modality in robot learning. We introduce:.1. 🦾A
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RT @_tonytao_: Thrilled to have received Best Paper Award at the EgoAct Workshop at RSS 2025! 🏆. We’ll also be giving a talk at the Imitati….
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RT @Haoyu_Xiong_: Your bimanual manipulators might need a Robot Neck 🤖🦒. Introducing Vision in Action: Learning Active Perception from Huma….
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RT @_tonytao_: 🦾 DexWild is now open-source!. Scaling up in-the-wild data will take a community effort, so let’s work together. Can’t wait….
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RT @yjy0625: Introducing Mobi-π: Mobilizing Your Robot Learning Policy. Our method:.✈️ enables flexible mobile skill chaining.🪶 without re….
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RT @li_yitang: 🤖Can a humanoid robot carry a full cup of beer without spilling while walking 🍺?. Hold My Beer !. Introducing Hold My Beer🍺:….
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RT @mengdaxu__: Can we collect robot dexterous hand data directly with human hand?. Introducing DexUMI:. 0 teleoperation and 0 re-targeting….
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RT @tylerlum23: 🧑🤖 Introducing Human2Sim2Robot! 💪🦾. Learn robust dexterous manipulation policies from just one human RGB-D video. Our Rea….
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RT @leggedrobotics: A legged mobile manipulator trained to play badminton with humans coordinates whole-body maneuvers and onboard percepti….
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RT @pathak2206: Maximizing Confidence Alone Improves Reasoning. Feels like the start of the "curiosity-driven learning" era for LLMs. I hav….
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RT @lchen915: One fundamental issue with RL – whether it’s for robots or LLMs – is how hard it is to get rewards. For LLM reasoning, we nee….
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RT @FahimTajwar10: RL with verifiable reward has shown impressive results in improving LLM reasoning, but what can we do when we do not hav….
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