4 papers
FlowDPG: Deterministic Policy Gradient on Flow Matching Policies for Real-World Manipulation
Kexin Shi, Junyao Shi, Poorvi Hebbar +5
Real-world reinforcement learning for robotic manipulation remains challenging, and this difficulty is amplified for flow matching policies: applying policy gradient methods to the…
3D-DLP: Self-Supervised 3D Object-Centric Scene Representation Learning
Ellina Zhang, Madhaven Iyengar, Amir Zadeh +4
We introduce 3D-DLP, a self-supervised object-centric representation learning model that decomposes scene-level RGB-D or voxel observations into a set of 3D latent particles. Build…
Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling
Tal Daniel, Carl Qi, Dan Haramati +5
We introduce Latent Particle World Model (LPWM), a self-supervised object-centric world model scaled to real-world multi-object datasets and applicable in decision-making. LPWM aut…
Local Policies Enable Zero-shot Long-horizon Manipulation
Murtaza Dalal, Min Liu, Walter Talbott +4
Sim2real for robotic manipulation is difficult due to the challenges of simulating complex contacts and generating realistic task distributions. To tackle the latter problem, we in…