Vignesh Prasad
483 posts

Vignesh Prasad
@FatAndFurious42
Trying to teach robots how to shitpost Robot Learning Post-Doc w/ @GeorgiaChal Ex: @ias_tudarmstadt @TCSResearch @iiit_hyderabad (Also a comedian)


Solving contact-rich manipulation tasks even under disco light🪩? Incorporate multiple sensors using MSDP and obtain a robust policy in under 55 min! Excited to share our work accepted at RA-L: Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning

Can’t agree more. Robotics is to make integration work! But now even when existing components integrate seamlessly for a new task, there’s a push to invent something unnecessary just for the sake of novelty. This is why papers read more marketing today. x.com/breadli428/sta…



this is an actual research paper 😭




Multisensory Dynamic Pretraining (MSDP) is based on masked autoencoding and cross-sensor prediction, leading to rich sensor fusion. Usage of the FT-sensor boosts performance by 14%! Our approach is flexible regarding the number and type of sensors. More Details in the Preprint!


CoRL 2026 is cutting review cycles to <3 months to keep pace with the rapid progress in robotics. @ieee_ras_icra @IROS2025 — maybe it’s time to rethink too. It makes little sense to discuss papers at conferences a year after submission, when they’re already outdated.

We present "6DOPE-GS: Fast, accurate 6D object pose tracking via Gaussian Splatting" during the Tuesday 21st afternoon poster session, w/ Vignesh Prasad @FatAndFurious42 and led by Yufeng Jin @yjin_1118 🌐Project page: pearl-robot-lab.github.io/6dope-gs 📄Paper: arxiv.org/abs/2412.01543


Just to recap: We found out today that an LLM that fits on a high-end consumer GPU, when trained on specific biological data, can discover a novel method to make cancer tumors more responsive to immunotherapy. Confirmed novel discovery (not present in existing literature). Experimentally validated in living cells. This is AI generating novel science. The moment has finally arrived.


PSA for the robotics community: Stop labeling affordances or distilling them from VLMs. Extract affordances from bimanual human videos instead! Excited to share 2HandedAfforder: Learning Precise Actionable Bimanual Affordances from Human Videos, accepted at #ICCV2025! 🎉 🧵1/5


Do you regret not learning Abacus ?? --- YES/NO




