16 citations · 32 across the 11 of their papers we have counts for
6 papers · 1 filter
Robot Critics that Sweat the Small Stuff
Sruthi Sudhakar, Junbang Liang, Sreehari Rammohan +3
Large vision-language models contain several priors about the world and object interactions, making them useful critics during inference to steer robot policies towards success. Ho…
RoboDream: Compositional World Models for Scalable Robot Data Synthesis
Junjie Ye, Rong Xue, Basile Van Hoorick +6
Scaling robot learning requires large-scale, diverse demonstrations, yet real-world data collection via teleoperation remains prohibitively expensive and time-consuming. While vide…
Capturing Visual Environment Structure Correlates with Control Performance
Jiahua Dong, Yunze Man, Pavel Tokmakov +1
The choice of visual representation is key to scaling generalist robot policies. However, direct evaluation via policy rollouts is expensive, even in simulation. Existing proxy met…
AnchorDream: Repurposing Video Diffusion for Embodiment-Aware Robot Data Synthesis
Junjie Ye, Rong Xue, Basile Van Hoorick +4
The collection of large-scale and diverse robot demonstrations remains a major bottleneck for imitation learning, as real-world data acquisition is costly and simulators offer limi…
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…