Probably helps with exploration...

Nice "Rationality" lectures by Stephen Pinker and Guests: harvard.hosted.panopto.com/Panopto/Pages/…

Incredibly thrilled that our work on robotic grasping is a Finalist for Best Paper Awards at #ICRA_2020! Looking forward to the conference starting tomorrow! w/ Arsalan Mousavian Clemens Eppner Chris Paxton Dieter Fox Paper: arxiv.org/abs/1912.03628 NVIDIA blog: nvda.ws/36O2hX5

"And then a strange thing happened." The design pitfall known as the "wheel of reincarnation"... (from "On the Design of Display Processors" by Myer and Sutherland, 1968. cva.stanford.edu/classes/cs99s/…)

ACRONYM: A Large-Scale Grasp Dataset Based on Simulation arxiv.org/abs/2011.09584

Motion planning methods need 3D model of the scene/voxelization to check for collisions. Inspired by implicit methods, we propose a model that can check 500K collision queries of unknown object against the scene in one fwd pass. Project Link: tinyurl.com/yx9we46n

We have a great lineup of speakers for our #RSS2021 workshop on understanding gaps between academic and industrial applications in #Robotics. Consider submitting extended abstracts. Deadline is tomorrow. sites.google.com/view/rss-ai-ma…

Very excited to share our new review paper on deep learning in grasping, focusing on 6-DoF grasping works. Got to work with some great collaborators including Clemens Eppner Arsalan Mousavian Juxi Leitner Jeannette Bohg We hope the community can find our review useful! arxiv.org/abs/2207.02556

Motion Policy Networks abs: arxiv.org/abs/2210.12209 project page: mpinets.github.io

Annoyed that existing motion generation systems promise the world but leave you with endless parameter tuning? Announcing Motion Policy Networks (MπNets)! 🤖🦾🪩   Website (code, data): mpinets.github.io Paper: arxiv.org/abs/2210.12209   W/ Adithya Murali, Clemens Eppner, et. al

Robotics AI researchers🤖 have you wondered - are we really making progress in robotics? Will we have our own ImageNet moment? Are we using the right benchmarks, if any? Join us for our Benchmarking workshop on Thursday, Dec 15 at Conference on Robot Learning sites.google.com/view/corl22ben… 🧵👇

Excited to release Motion Policy Networks! w/ Adam Fishman 🐟🤖🐠 Clemens Eppner If you are at Conference on Robot Learning in NZ, checkout Adam’s presentation on Sunday, Dec 18, 9:35AM (Session 5)

CabiNet: Scaling Neural Collision Detection for Object Rearrangement with Procedural Scene Generation abs: arxiv.org/abs/2304.09302 project page: cabinet-object-rearrangement.github.io

At NVIDIA AI research, we have been working on general-purpose robotic rearrangement 🤖 Today, we are announcing CabiNet, our recent work on scaling object rearrangement in clutter with synthetic data: cabinet-object-rearrangement.github.io w/ Arsalan Mousavian, Clemens Eppner, Adam Fishman 🐟🤖🐠

Excited to share our latest work on industrial pick-and-place in collaboration with Intrinsic announced at #Automate2024 Technical blog: developer.nvidia.com/blog/automatin… w/ Clemens Eppner Balakumar Sundaralingam Xuning Yang

In robotics benchmarks are rarely shared. New eval setups are created for each new project, a stark difference from evals in broader ML. But generalist policies share a problem statement: do any task in any environment. Can generalist capabilities make robot evaluation easier?