Paper
Exp-Force: Experience-Conditioned Pre-Grasp Force Selection with Vision-Language Models
Authors
Siqi Shang, Minchao Huang, Bill Fan, Lillian Chin
Abstract
Accurate pre-contact grasp force selection is critical for safe and reliable robotic manipulation. Adaptive controllers regulate force after contact but still require a reasonable initial estimate. Starting a grasp with too little force requires reactive adjustment, while starting a grasp with too high a force risks damaging fragile objects. This trade-off is particularly challenging for compliant grippers, whose contact mechanics are difficult to model analytically. We propose Exp-Force, an experience-conditioned framework that predicts the minimum feasible grasping force from a single RGB image. The method retrieves a small set of relevant prior grasping experiences and conditions a vision-language model on these examples for in-context inference, without analytic contact models or manually designed heuristics. On 129 object instances, ExpForce achieves a best-case MAE of 0.43 N, reducing error by 72% over zero-shot inference. In real-world tests on 30 unseen objects, it improves appropriate force selection rate from 63% to 87%. These results demonstrate that Exp-Force enables reliable and generalizable pre-grasp force selection by leveraging prior interaction experiences. http://expforcesubmission.github.io/Exp-Force-Website/
Metadata
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.08668v1</id>\n <title>Exp-Force: Experience-Conditioned Pre-Grasp Force Selection with Vision-Language Models</title>\n <updated>2026-03-09T17:41:22Z</updated>\n <link href='https://arxiv.org/abs/2603.08668v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.08668v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Accurate pre-contact grasp force selection is critical for safe and reliable robotic manipulation. Adaptive controllers regulate force after contact but still require a reasonable initial estimate. Starting a grasp with too little force requires reactive adjustment, while starting a grasp with too high a force risks damaging fragile objects. This trade-off is particularly challenging for compliant grippers, whose contact mechanics are difficult to model analytically. We propose Exp-Force, an experience-conditioned framework that predicts the minimum feasible grasping force from a single RGB image. The method retrieves a small set of relevant prior grasping experiences and conditions a vision-language model on these examples for in-context inference, without analytic contact models or manually designed heuristics. On 129 object instances, ExpForce achieves a best-case MAE of 0.43 N, reducing error by 72% over zero-shot inference. In real-world tests on 30 unseen objects, it improves appropriate force selection rate from 63% to 87%. These results demonstrate that Exp-Force enables reliable and generalizable pre-grasp force selection by leveraging prior interaction experiences. http://expforcesubmission.github.io/Exp-Force-Website/</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.RO'/>\n <published>2026-03-09T17:41:22Z</published>\n <arxiv:primary_category term='cs.RO'/>\n <author>\n <name>Siqi Shang</name>\n </author>\n <author>\n <name>Minchao Huang</name>\n </author>\n <author>\n <name>Bill Fan</name>\n </author>\n <author>\n <name>Lillian Chin</name>\n </author>\n </entry>"
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