CoRMA modifies RMA by replacing raw parameter adaptation with inference of a 6D semantic contact context via a causal Transformer trained with semantic regression and force-regime contrastive loss, yielding higher real-world success than FORGE baselines on PegInsert, GearMesh, and NutThread under ta
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we trained a large convolutional neural network to predict the probability that task-space motion of the gripper will result in successful grasps, using only monocular camera images and independently of camera calibration or the current robot pose. This requires the network to observe the spatial relationship between the gripper and objects in the scene, thus learning hand-eye coordination. We then use this network to servo the gripper in real time to achieve successful grasps. To train our network, we collected over 800,000 grasp attempts over the course of two months, using between 6 and 14 robotic manipulators at any given time, with differences in camera placement and hardware. Our experimental evaluation demonstrates that our method achieves effective real-time control, can successfully grasp novel objects, and corrects mistakes by continuous servoing.
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cs.RO 3years
2026 3verdicts
UNVERDICTED 3roles
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unclear 1representative citing papers
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.
The paper reviews limits in AI vision for robotics and describes work-in-progress on bridging sim-to-real domain gaps by linking real and synthetic training data.
citing papers explorer
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CoRMA: Contrastive RMA for Contact-Rich Meta-Adaptation
CoRMA modifies RMA by replacing raw parameter adaptation with inference of a 6D semantic contact context via a causal Transformer trained with semantic regression and force-regime contrastive loss, yielding higher real-world success than FORGE baselines on PegInsert, GearMesh, and NutThread under ta
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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.
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Efficiently Linking Real Scenes with Synthetic Data Generation for AI-based Cognitive Robotics and Computer Vision Applications
The paper reviews limits in AI vision for robotics and describes work-in-progress on bridging sim-to-real domain gaps by linking real and synthetic training data.