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GelFusion: Enhancing Robotic Manipulation under Visual Constraints via Visuotactile Fusion
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Visuotactile sensing offers rich contact information that can help mitigate performance bottlenecks in imitation learning, particularly under vision-limited conditions, such as ambiguous visual cues or occlusions. Effectively fusing visual and visuotactile modalities, however, presents ongoing challenges. We introduce GelFusion, a framework designed to enhance policies by integrating visuotactile feedback, specifically from high-resolution GelSight sensors. GelFusion using a vision-dominated cross-attention fusion mechanism incorporates visuotactile information into policy learning. To better provide rich contact information, the framework's core component is our dual-channel visuotactile feature representation, simultaneously leveraging both texture-geometric and dynamic interaction features. We evaluated GelFusion on three contact-rich tasks: surface wiping, peg insertion, and fragile object pick-and-place. Outperforming baselines, GelFusion shows the value of its structure in improving the success rate of policy learning.
Forward citations
Cited by 3 Pith papers
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FELT: Generating Tactile Signals from Vision for Visuo-Tactile Manipulation
FELT predicts finger pressure maps from RGB images and uses them or their learned features to improve manipulation policies without real tactile sensors at deployment.
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TactX: Learning Shared Tactile Representations Across Diverse Sensors
TactX learns a shared latent representation across three tactile sensor modalities via joint training on paired contacts, enabling zero-shot policy transfer and higher success on pick-and-place, insertion, wiping, and...
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Surformer v2: A Multimodal Classifier for Surface Understanding from Touch and Vision
Surformer v2 combines an ImageNet-pretrained CNN for vision with a handcrafted-feature transformer for touch, fuses their outputs via learned weights, and reports 97.4% accuracy with 0.0239 ms inference on Touch and Go.
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