CoRMA enables within-episode adaptation for contact-rich robotic assembly by inferring semantic contact context with a causal Transformer and force-regime contrastive objective, retaining higher real success than FORGE baselines under target-pose noise.
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
2 Pith papers cite this work. Polarity classification is still indexing.
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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 2years
2026 2verdicts
UNVERDICTED 2roles
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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.
citing papers explorer
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CoRMA: Contrastive RMA for Contact-Rich Meta-Adaptation
CoRMA enables within-episode adaptation for contact-rich robotic assembly by inferring semantic contact context with a causal Transformer and force-regime contrastive objective, retaining higher real success than FORGE baselines under target-pose noise.
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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.