Top Tweets for #rlbench
'basketball_in_hoop'; one of many new tasks joining the #RLBench family of 100+ tasks in V1.2. Coming early November! ๐ค
RLBench is still the hardest manipulation sim-benchmark to date due to its large-scale focus on vision, sparse rewards, and multi-stage tasks.
I have quite a few qualms with MuJoCo, and this is certainly one of the main ones. Might be a good time for folks to check out #RLBench for benchmarking their manipulation algorithms. ๐ (https://t.co/cBBItb0OAl)
Uses Bullet under the hood.
Glad to see some discussion about the rejection of the use of proprietary tech (MuJoCo, Unity etc) in public benchmarks, while open source alternatives such as PyBullet are available: https://t.co/ETo93tO3Qj
After many requests, #RLBench task building video tutorials are here! This complements the text-based tutorials that are already available on GitHub.
https://t.co/4LnSyDZCsE
We are thrilled to announce RLBench: an ambitious large-scale benchmark and learning environment for vision-guided manipulation with 100 unique, hand-design tasks!
Paper: https://t.co/l6QyaZKocy
Video: https://t.co/5GY4lBEg9L
w/ @StephenLJames, Z. Ma, D. Arrojo, @AjdDavison
#RLBench tasks/features (3/n). This bin emptying task is one of the 100 tasks of RLBench. The goal is to move the objects from the grey bin to one of the specified coloured bins. The robot must infer the goal from either the textual description of the task or from the demos.
#RLBench tasks/features (2/n). RLBench comes with support for domain randomisation of both visual and dynamics with 1 line of code, allowing for rapid sim-to-real experiments!
https://t.co/cBBItb0OAl
Over the next few weeks, I'll periodically post various tasks/features from #RLBench. First up, a shape sorting task! This one would be a great task to try for a sim-to-real project! Both vision and proprioceptive feedback needed for this one.
https://t.co/cBBItb0OAl
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