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Feng Chen

@Winniechen02

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Ph.D. student. at the University of Hong Kong, advised by prof. Yi Ma

Central & Western District
Joined July 2023
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@Winniechen02
Feng Chen
4 months
🚀 Excited to share our new work on generative simulation: On the Evaluation of Generative Robotic Simulations! In this work, we propose a novel framework for evaluating generative robotic simulations from various aspects. 🤖️
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@Winniechen02
Feng Chen
14 days
RT @TianzheC: [1/n] 🧐@deepseek_ai #DeepSeekR1 has shown the power of RL without SFT. But what does RL learns differently than SFT? Our ans…
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@Winniechen02
Feng Chen
1 month
RT @zhou_xian_: In our initial release, we claimed that Genesis is over an order of magnitude faster than existing GPU-accelerated robotic…
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@Winniechen02
Feng Chen
2 months
If the robotics community lacks a simulator enough to convince everyone to use it by now, then from now on, we will reach a consensus that Genesis will be the best one.
@zhou_xian_
Zhou Xian
2 months
Everything you love about generative models — now powered by real physics! Announcing the Genesis project — after a 24-month large-scale research collaboration involving over 20 research labs — a generative physics engine able to generate 4D dynamical worlds powered by a physics simulation platform designed for general-purpose robotics and physical AI applications. Genesis's physics engine is developed in pure Python, while being 10-80x faster than existing GPU-accelerated stacks like Isaac Gym and MJX. It delivers a simulation speed ~430,000 faster than in real-time, and takes only 26 seconds to train a robotic locomotion policy transferrable to the real world on a single RTX4090 (see tutorial: . The Genesis physics engine and simulation platform is fully open source at We'll gradually roll out access to our generative framework in the near future. Genesis implements a unified simulation framework all from scratch, integrating a wide spectrum of state-of-the-art physics solvers, allowing simulation of the whole physical world in a virtual realm with the highest realism. We aim to build a universal data engine that leverages an upper-level generative framework to autonomously create physical worlds, together with various modes of data, including environments, camera motions, robotic task proposals, reward functions, robot policies, character motions, fully interactive 3D scenes, open-world articulated assets, and more, aiming towards fully automated data generation for robotics, physical AI and other applications. Open Source Code: Project webpage: Documentation: 1/n
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@Winniechen02
Feng Chen
2 months
RT @zhou_xian_: Everything you love about generative models — now powered by real physics! Announcing the Genesis project — after a 24-mon…
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@Winniechen02
Feng Chen
3 months
@lfqirrrrr congrats!
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@Winniechen02
Feng Chen
4 months
RT @hkudatascience: 【Grand Opening of IDS New Premises on Oct 29 – Share the Joy with Us for Its Promising Future Dev't amidst Its 3rd Birt…
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@Winniechen02
Feng Chen
4 months
RT @hkudatascience: 【IDS PanDA-TAs had fun - Thank you for joining us today @ HKU Info Day 2024🐼】 Hope you had enjoyed our activities, ta…
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@Winniechen02
Feng Chen
4 months
RT @YiMaTweets: Giving a plenary speech at the Chinese pattern recognition and computer vision conference in Urumqi…
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@Winniechen02
Feng Chen
4 months
@DesFrontierTech We are evaluating the data set of the simulation environment, so it does not involve the problem you mentioned.
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@Winniechen02
Feng Chen
4 months
@greentomorro Since the current generative simulation pipelines lack reasonable evaluation, we think that we need to propose a good evaluation model.
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@Winniechen02
Feng Chen
4 months
@THU_PuHua Thanks, Pu!
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@Winniechen02
Feng Chen
4 months
@Gyrocopter_MK0 @THU_PuHua @EnochDynamo @yang_yanchao @YiMaTweets @HarryXu12 🔍 Check out the details and results in our paper! 🔗
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@Winniechen02
Feng Chen
4 months
RT @THU_PuHua: 🔥Generate and solve robot simulation tasks with multi-modal LLMs! 🤖Introduce our work to be presented at #CoRL2024 in Munich…
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@Winniechen02
Feng Chen
1 year
RT @_akhaliq: RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation paper page:
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