ForeTac: Predictive Contact Guidance for Generative Robot Po
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: ForeTac: Predictive Contact Guidance for Generative Robot Policies. ForeTac is a predictive contact-guidance framework that improves robot task success rates by anticipating and evaluating contact outcomes through tactile feedback. It uses a represent-predict-evaluate-guide loop, with a …
Generated from attributed reports · Updated 1 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · Reported success rate: 36.3 percent · Basis not reported · Differing source assertions
“ide action generation while the pretrained model parameters remain frozen. We evaluate ForeTac on four real-robot tasks (planar and curved-surface wiping, card swiping, and fragile-object grasping) with diffusion and flow-matching policies. Across these tasks, ForeTac raises the average task success rate from 36.3% to 71.3% and the average contact success rate (CSR) from 20.0% to 66.3% compared with vision-only Diffu”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 20 percent · Basis not reported · Differing source assertions
“ide action generation while the pretrained model parameters remain frozen. We evaluate ForeTac on four real-robot tasks (planar and curved-surface wiping, card swiping, and fragile-object grasping) with diffusion and flow-matching policies. Across these tasks, ForeTac raises the average task success rate from 36.3% to 71.3% and the average contact success rate (CSR) from 20.0% to 66.3% compared with vision-only Diffu”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 71.3 percent · Basis not reported · Differing source assertions
“ide action generation while the pretrained model parameters remain frozen. We evaluate ForeTac on four real-robot tasks (planar and curved-surface wiping, card swiping, and fragile-object grasping) with diffusion and flow-matching policies. Across these tasks, ForeTac raises the average task success rate from 36.3% to 71.3% and the average contact success rate (CSR) from 20.0% to 66.3% compared with vision-only Diffu”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 66.3 percent · Basis not reported · Differing source assertions
“ide action generation while the pretrained model parameters remain frozen. We evaluate ForeTac on four real-robot tasks (planar and curved-surface wiping, card swiping, and fragile-object grasping) with diffusion and flow-matching policies. Across these tasks, ForeTac raises the average task success rate from 36.3% to 71.3% and the average contact success rate (CSR) from 20.0% to 66.3% compared with vision-only Diffu”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsForeTac: Predictive Contact Guidance for Generative Robot Policies
ForeTac is a predictive contact-guidance framework that improves robot task success rates by anticipating and evaluating contact outcomes through tactile feedback. It uses a represent-predict-evaluate-guide loop, with a contact-quality objective learned from physical interactions. Evaluated on four real-robot tasks, ForeTac increases average task success from 36.3% to 71.3% and contact success rate from 20.0% to 66.3% compared to vision-only Diffusion Policy.
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