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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
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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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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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.
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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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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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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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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RT @YiMaTweets: Giving a plenary speech at the Chinese pattern recognition and computer vision conference in Urumqi…
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@DesFrontierTech We are evaluating the data set of the simulation environment, so it does not involve the problem you mentioned.
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@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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@Gyrocopter_MK0 @THU_PuHua @EnochDynamo @yang_yanchao @YiMaTweets @HarryXu12 🔍 Check out the details and results in our paper! 🔗
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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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RT @_akhaliq: RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation paper page:
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