# Residual Reinforcement Learning

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medRxiv Oct 24, 2026
Sepsis glucose digital twins stop waiting for error thresholds: a frozen PatchTST plus reinforcement-learning residual fix improved one-step forecasts in all 10 ICU patients
Glucose digital twins in septic intensive care previously refit the model only after forecast error crossed a threshold, a reactive correction. This work reports a proactive workflow: a frozen PatchTST foundation model produces a one-step forecast, and an Actor–Critic reinforcement-learning agent uses the last 30-minute glucose window and recent residual history to apply a correction bounded at ±0.05 to that first step, which is then rolled out recursively over 15 steps. On minute-by-minute continuous glucose monitoring from 10 septic ICU patients, the pretrained agent lowered one-step forecast error in all 10 without patient-specific retraining, and mean absolute percentage error on the source patient's test set fell from 0.49% to 0.34%.
Glucose digital twins in septic intensive care previously refit the model only after forecast error crossed a threshold, a reactive correction. This work reports a proactive workflow: a frozen PatchTST foundation model produces a one-step forecast, and an Actor–Critic reinforcement-learning agent uses the last 30-minute glucose window and recent residual history to apply a correction bounded at ±0.05 to that first step, which is then rolled out recursively over 15 steps. On minute-by-minute continuous glucose monitoring from 10 septic ICU patients, the pretrained agent lowered one-step forecast error in all 10 without patient-specific retraining, and mean absolute percentage error on the source patient's test set fell from 0.49% to 0.34%.
University of South Carolina
Cao Xiang（First author）Qi Wang（Corresponding author）
Digital Twin Residual Reinforcement Learning PatchTST Continuous Glucose Forecasting Data Assimilation
arXiv Oct 9, 2026
CAPABLE lifts a frozen VLA from 24.8% to 59.3% success on an actuator excluded from fault training
CAPABLE is a capability-aware adaptation framework for frozen vision-language-action policies: a temporal encoder shared across joints infers from command-response history and live Jacobian grounding how much commanded motion each joint still realizes, a self-supervised physical-prediction objective ties that representation to behavior, and a bounded residual reinforcement-learning policy corrects the VLA arm action without fault labels or faulty-joint identifiers; across 28 LIBERO tasks it raises success on an actuator excluded from fault training from 24.8% to 59.3%, 17.4 points above a parameter-matched global-history baseline, while preserving healthy performance.
CAPABLE is a capability-aware adaptation framework for frozen vision-language-action policies: a temporal encoder shared across joints infers from command-response history and live Jacobian grounding how much commanded motion each joint still realizes, a self-supervised physical-prediction objective ties that representation to behavior, and a bounded residual reinforcement-learning policy corrects the VLA arm action without fault labels or faulty-joint identifiers; across 28 LIBERO tasks it raises success on an actuator excluded from fault training from 24.8% to 59.3%, 17.4 points above a parameter-matched global-history baseline, while preserving healthy performance.
University of North Texas University of Bremen
Mohammad Khoshnazar（First author）
CAPABLE Residual Reinforcement Learning Vision-Language-Action Policy Fault-Tolerant Robot Control LIBERO
arXiv Sep 28, 2026
Berkeley team's Morphometric Imitation retargets human hand-object interaction to three-, four-, and five-fingered robot hands, reaching 89.3% zero-shot success in 300 real-world trials
The work presents Morphometric Imitation, a three-stage framework that first kinematically retargets human hand-object interaction to robot hands of different morphologies while preserving demonstrated contacts via morphometric optimization (MMO), then uses residual reinforcement learning with object pose and contact information to produce dynamically feasible demonstrations, and finally distills them into visuomotor policies; across three robot hands and ten GRAB trajectories, MMO improves contact F1 over the strongest of five baselines by at least 8 points, downstream dynamic retargeting success by as much as 35 points, and the distilled policies achieve 89.3% zero-shot success in 300 real-world trials on 30 objects.
The work presents Morphometric Imitation, a three-stage framework that first kinematically retargets human hand-object interaction to robot hands of different morphologies while preserving demonstrated contacts via morphometric optimization (MMO), then uses residual reinforcement learning with object pose and contact information to produce dynamically feasible demonstrations, and finally distills them into visuomotor policies; across three robot hands and ten GRAB trajectories, MMO improves contact F1 over the strongest of five baselines by at least 8 points, downstream dynamic retargeting success by as much as 35 points, and the distilled policies achieve 89.3% zero-shot success in 300 real-world trials on 30 objects.
University of California, Berkeley
Tara Sadjadpour（First author）
Morphometric Imitation Morphometric Optimization Residual Reinforcement Learning Sim-to-Real Transfer Motion Retargeting

You’re all caught up

Sepsis glucose digital twins stop waiting for error thresholds: a frozen PatchTST plus reinforcement-learning residual fix improved one-step forecasts in all 10 ICU patients

CAPABLE lifts a frozen VLA from 24.8% to 59.3% success on an actuator excluded from fault training

Berkeley team's Morphometric Imitation retargets human hand-object interaction to three-, four-, and five-fingered robot hands, reaching 89.3% zero-shot success in 300 real-world trials