4 papers
TT4D: A Pipeline and Dataset for Table Tennis 4D Reconstruction From Monocular Videos
Nima Rahmanian, Daniel Kienzle, Thomas Gossard +3
We present TT4D, a large-scale, high-fidelity table tennis dataset. It provides hours of reconstructed singles and doubles gameplay from monocular broadcast videos, featurin…
HITTER: A HumanoId Table TEnnis Robot via Hierarchical Planning and Learning
Zhi Su, Bike Zhang, Nima Rahmanian +5
Humanoid robots have recently achieved impressive progress in locomotion and whole-body control, yet they remain constrained in tasks that demand rapid interaction with dynamic env…
LATTE-MV: Learning to Anticipate Table Tennis Hits from Monocular Videos
Daniel Etaat, Dvij Kalaria, Nima Rahmanian +1
Physical agility is a necessary skill in competitive table tennis, but by no means sufficient. Champions excel in this fast-paced and highly dynamic environment by anticipating the…
Role of Uncertainty in Anticipatory Trajectory Prediction for a Ping-Pong Playing Robot
Nima Rahmanian, Michael Gupta, Renzo Soatto +4
Robotic interaction in fast-paced environments presents a substantial challenge, particularly in tasks requiring the prediction of dynamic, non-stationary objects for timely and ac…