RoboFlow4D is an end-to-end lightweight flow world model that predicts multi-frame 3D flows from visual observations and textual instructions to provide explicit planning for real-time robotic manipulation.
Learning robotic manipulation policies from point clouds with conditional flow matching
4 Pith papers cite this work. Polarity classification is still indexing.
citation-role summary
citation-polarity summary
fields
cs.RO 4verdicts
UNVERDICTED 4roles
background 1polarities
background 1representative citing papers
A vision-guided hybrid rigid-soft manipulator achieves consistent sub-2cm reaching in unseen cluttered environments via shape-aware planning and learning control without environment-specific retraining.
Trajectory consistency training, smoothness regularization, and higher-order integration for flow matching policies deliver 60-70% success on long-horizon real-robot tasks where baselines achieve 0%.
Framework generates force-informed sim data from one demo to train compliant visuomotor flow matching policies, showing reliable contact on real-robot block flipping and bi-manual tasks.
citing papers explorer
-
RoboFlow4D: A Lightweight Flow World Model Toward Real-Time Flow-Guided Robotic Manipulation
RoboFlow4D is an end-to-end lightweight flow world model that predicts multi-frame 3D flows from visual observations and textual instructions to provide explicit planning for real-time robotic manipulation.
-
HyReach: Vision-Guided Hybrid Manipulator Reaching in Unseen Cluttered Environments
A vision-guided hybrid rigid-soft manipulator achieves consistent sub-2cm reaching in unseen cluttered environments via shape-aware planning and learning control without environment-specific retraining.
-
Trajectory-Consistent Flow Matching for Robust Visuomotor Policy Learning
Trajectory consistency training, smoothness regularization, and higher-order integration for flow matching policies deliver 60-70% success on long-horizon real-robot tasks where baselines achieve 0%.
-
Flow with the Force Field: Learning 3D Compliant Flow Matching Policies from Force and Demonstration-Guided Simulation Data
Framework generates force-informed sim data from one demo to train compliant visuomotor flow matching policies, showing reliable contact on real-robot block flipping and bi-manual tasks.