Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

🤖🤖🤖 Following RoboVerse, we introduce another work focused on Robotic Tactile Simulation - Taccel Simulator. Taccel is a high-performance simulation platform for vision-based tactile sensors and robots. 🚀🚀🚀 Boosted by Nvidia Warp, we optimize Taccel with highly parallelized simulations and support 900fps simulation with 4k+ parallel training envs. 🤝🤝🤝...

10,660 Aufrufe • vor 1 Jahr •via X (Twitter)

4 Kommentare

Profilbild von Siyuan Huang
Siyuan Huangvor 1 Jahr

Taccel is a joint work with PKU CoRE lab (PI, Prof. Yixin Zhu) and UCLA AIVC Lab (PI, Prof. Chenfanfu Jiang), shout out to the wonderful collaborators and project leader @aidenli_thu

Profilbild von Junshan Huang
Junshan Huangvor 1 Jahr

Awesome work! Just starred the repo — looking forward to trying it out later.

Profilbild von LECCA Intern (Ø,G) 🍜
LECCA Intern (Ø,G) 🍜vor 1 Jahr

900fps + 4K parallel envs? That’s some next-level sim performance.

Profilbild von Siyuan Huang
Siyuan Huangvor 1 Jahr

Thank you for the interest! In this case, the total simulation speed is 900fps and ~0.22fps for each env. We are still optimizing it!

Ähnliche Videos

In my past research experience, finding or developing an appropriate simulation environment, dataset, and benchmark has always been a challenge. Missing features, limited support, or unexpected bugs often occupied my days and nights. Moreover, current simulation platforms are relatively fragmented—making it challenging to replicate the success of the RT-X dataset in unifying community efforts. Introducing RoboVerse, we provide a unified platform, dataset, and benchmark for scalable and generalizable robot learning. We hope to build a shared foundation to combine the community efforts. RoboVerse includes: MetaSim: We carefully designed a configuration system and a universal interface to align current robotic simulators. With MetaSim, you can use any simulator with the same code—bringing together the community’s diverse efforts under one framework! RoboVerse Dataset and Benchmark: We unify popular simulation environments and benchmarks into a single cohesive system and introduce the RoboVerse dataset—a large-scale, high-quality synthetic dataset. Additionally, we propose a standardized benchmark across both imitation learning and reinforcement learning. A cool feature enabled by our unified framework: Hybrid Simulation! You can now integrate physics engines and renderers from different simulators—e.g., using MuJoCo precise physics with Isaac photorealistic rendering. This not only elevates simulation fidelity but also significantly enhances real-world transfer performance across complex robotic applications. Hopefully, our team’s efforts could serve the robotic community to thrive vibrantly in the years to come. RoboVerse is open-sourced🥳!!! Project Page: Documentation: Github Repo: Paper:

Haoran Geng

84,215 Aufrufe • vor 1 Jahr