47 citations · 80 across the 19 of their papers we have counts for
9 papers · 1 filter
Video Generators are Robot Policies
Junbang Liang, Pavel Tokmakov, Ruoshi Liu +4
Despite tremendous progress in dexterous manipulation, current visuomotor policies remain fundamentally limited by two challenges: they struggle to generalize under perceptual or b…
A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation
TRI LBM Team, Jose Barreiros, Andrew Beaulieu +79
Robot manipulation has seen tremendous progress in recent years, with imitation learning policies enabling successful performance of dexterous and hard-to-model tasks. Concurrently…
Real2Render2Real: Scaling Robot Data Without Dynamics Simulation or Robot Hardware
Justin Yu, Letian Fu, Huang Huang +5
Scaling robot learning requires vast and diverse datasets. Yet the prevailing data collection paradigm-human teleoperation-remains costly and constrained by manual effort and physi…
ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping
Shun Iwase, Zubair Irshad, Katherine Liu +8
Robotic grasping is a cornerstone capability of embodied systems. Many methods directly output grasps from partial information without modeling the geometry of the scene, leading t…
Is Your Imitation Learning Policy Better than Mine? Policy Comparison with Near-Optimal Stopping
David Snyder, Asher James Hancock, Apurva Badithela +6
Imitation learning has enabled robots to perform complex, long-horizon tasks in challenging dexterous manipulation settings. As new methods are developed, they must be rigorously e…
Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policies
Chen Xu, Tony Khuong Nguyen, Emma Dixon +7
Recent years have witnessed impressive robotic manipulation systems driven by advances in imitation learning and generative modeling, such as diffusion- and flow-based approaches.…