PREM: Prediction-Regulated Memory for Robot Manipulation
Overview
This research introduces Prediction-Regulated Memory (PREM), a framework that leverages action-conditioned future prediction to guide memory learning and updating in vision-language-action policies.
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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: 20.74 percent · Basis not reported · Differing source assertions
“each spatial location. The updated visual state supplies temporal context for action generation alongside current observations. Built on $\pi_{0.5}$, PREM improves average success rate from 9.63% to 20.74% on DOMINO dynamic manipulation tasks and from 14.4% to 83.8% on the RMBench M(1) memory-dependent setting. Further evaluations on LIBERO and LIBERO-Pro show improvements in general manipulation and robustness to s”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 83.8 percent · Basis not reported · Differing source assertions
“each spatial location. The updated visual state supplies temporal context for action generation alongside current observations. Built on $\pi_{0.5}$, PREM improves average success rate from 9.63% to 20.74% on DOMINO dynamic manipulation tasks and from 14.4% to 83.8% on the RMBench M(1) memory-dependent setting. Further evaluations on LIBERO and LIBERO-Pro show improvements in general manipulation and robustness to s”
Exact source · revision 1Source owner not reported
Reported quantity · mean inference overhead: 7.7 other · Basis not reported
“patial perturbations. PREM incurs a mean inference overhead of 7.7 ms on a single NVIDIA H800 GPU.”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 9.63 percent · Basis not reported · Differing source assertions
“each spatial location. The updated visual state supplies temporal context for action generation alongside current observations. Built on $\pi_{0.5}$, PREM improves average success rate from 9.63% to 20.74% on DOMINO dynamic manipulation tasks and from 14.4% to 83.8% on the RMBench M(1) memory-dependent setting. Further evaluations on LIBERO and LIBERO-Pro show improvements in general manipulation and robustness to s”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 14.4 percent · Basis not reported · Differing source assertions
“each spatial location. The updated visual state supplies temporal context for action generation alongside current observations. Built on $\pi_{0.5}$, PREM improves average success rate from 9.63% to 20.74% on DOMINO dynamic manipulation tasks and from 14.4% to 83.8% on the RMBench M(1) memory-dependent setting. Further evaluations on LIBERO and LIBERO-Pro show improvements in general manipulation and robustness to s”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalPREM: Prediction-Regulated Memory for Robot Manipulation
This research introduces Prediction-Regulated Memory (PREM), a framework that leverages action-conditioned future prediction to guide memory learning and updating in vision-language-action policies.
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