Imitation policies that reliably execute most of a manipulation trajectory can still fail in localized regions requiring last-mile precision. Updating the full policy or exploring residual actions everywhere treats this local failure as a global learning problem. We introduce Adaptive Residual Correction (ARC), a lightweight post-training module that uses reinforcement learning (RL) to learn a residual correction on top of a frozen policy while using sparse human corrections to structure autonomous exploration. Rather than treating human corrections as imitation targets, ARC uses pre-intervention states to determine 'when' residual control should become active and correction geometry to determine 'where' in residual action space autonomous refinement should explore; task reward determines 'what' residual is ultimately executed. Across three precision-manipulation tasks on a real robot, ARC improves average task success from 33.3% to 75.0%. The human corrections define task-dependent 2-3 dimensional subspaces of the seven-joint residual space that retain 96.0-97.7% of squared correction magnitude. Together, these results show that sparse human corrections can localize and constrain autonomous post-training without requiring the observed correction itself to serve as the final action target.
Figures & tables
Figure 1: ARC overview. Sparse human corrections train a pre-intervention gate and define a low-dimensional corrective subspace, which together constrain autonomous exploration in time and action space. Task reward trains the residual actor, whose gated correction is added to the frozen ACT action at deployment.
Figure 2: Performance on a real robot. Base ACT, direct supervised seven-joint residual correction, and ARC over 12 trials per method and task. Labels show exact successes; error bars are 95% Wilson confidence intervals.
Figure 3: WHEN and WHERE. (a) On 10 untouched autonomous base-policy rollouts, a gate trained on correction-active states never activates, while the pre-intervention gate activates on all 10 (gate activation, not task success). (b) Held-out gate discrimination and the fraction of autonomous-exploration timesteps for which the gate is open. (c) The selected 3 -, 2 -, and 2 -D subspaces retain 97.3% , 96.0% , and 97.7% of squared correction magnitude for tray, marker, and cup, respectively. (d) Marker corrections, exploration probes, and deployed ARC residuals in the learned corrective basis; PC denotes principal component.
Figure 4: WHAT: same support, different corrections. (a) Within BH , supervised prediction reduces held-out human-residual error only from 1.98∘ to 1.90∘ ( 4.0% ), while oracle projection gives a 1.23∘ lower bound. (b) On the same 2,499 gate-open states, median max-joint residual magnitude is 0.52∘ for supervision and 0.14∘ for ARC ( 3.7× ). (c) With the same BH and residual bounds, PC 1 sign agreement is 38% on non-trivial states, while 22% of supervised versus 0% of ARC predictions reach at least half the allowed bound.