EquiVLA is the first general framework for end-to-end SO(2)-equivariant VLA models using EquiPerceptor and EquiActor modules, reporting improved success rates on LIBERO, CALVIN, and real-robot benchmarks.
Title resolution pending
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
verdicts
UNVERDICTED 6roles
background 2polarities
background 2representative citing papers
DreamGen trains robot policies on synthetic trajectories from adapted video world models, enabling a humanoid robot to perform 22 new behaviors in seen and unseen environments from a single pick-and-place teleoperation dataset.
Conformal calibration produces orbit-valid trust horizons for equivariant world models, with zero violations in 50 audits and non-vacuous certificates on 2D/3D substrates.
SID achieves approximately 90% success on six real-world manipulation tasks with only two demonstrations under out-of-distribution initializations, with less than 10% performance drop under distractors and disturbances.
FLARE integrates predictive latent world modeling into diffusion transformer policies for robots, delivering up to 26% gains on multitask manipulation benchmarks and enabling co-training with action-free human videos.
StereoPolicy fuses left-right image features via cross-attention to deliver consistent gains over RGB, RGB-D, point cloud, and multi-view baselines in simulation and real-robot manipulation tasks.
citing papers explorer
-
EquiVLA: A General Framework for Rotationally Equivariant Vision-Language-Action Models
EquiVLA is the first general framework for end-to-end SO(2)-equivariant VLA models using EquiPerceptor and EquiActor modules, reporting improved success rates on LIBERO, CALVIN, and real-robot benchmarks.
-
DreamGen: Unlocking Generalization in Robot Learning through Video World Models
DreamGen trains robot policies on synthetic trajectories from adapted video world models, enabling a humanoid robot to perform 22 new behaviors in seen and unseen environments from a single pick-and-place teleoperation dataset.
-
Conformal Orbit-Valid Trust Horizons for Equivariant World Models
Conformal calibration produces orbit-valid trust horizons for equivariant world models, with zero violations in 50 audits and non-vacuous certificates on 2D/3D substrates.
-
SID: Sliding into Distribution for Robust Few-Demonstration Manipulation
SID achieves approximately 90% success on six real-world manipulation tasks with only two demonstrations under out-of-distribution initializations, with less than 10% performance drop under distractors and disturbances.
-
FLARE: Robot Learning with Implicit World Modeling
FLARE integrates predictive latent world modeling into diffusion transformer policies for robots, delivering up to 26% gains on multitask manipulation benchmarks and enabling co-training with action-free human videos.
-
StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception
StereoPolicy fuses left-right image features via cross-attention to deliver consistent gains over RGB, RGB-D, point cloud, and multi-view baselines in simulation and real-robot manipulation tasks.