RESETTLE: Robotic Recovery through Disagreement-Triggered Retrieval and Efficient Corrective Control

Yuxin Chen; Senqiao Yang; Zixuan Wang; Jinhui Ye; Changsheng Lu; Pengguang Chen; Shu Liu; Zhuotao Tian

cs.ROOct 8, 2026

RESETTLE: Robotic Recovery through Disagreement-Triggered Retrieval and Efficient Corrective Control

Authors: Yuxin Chen, Senqiao Yang, Zixuan Wang, Jinhui Ye, Changsheng Lu, Pengguang Chen, Shu Liu, Zhuotao Tian, +1 more

Organizations: The Hong Kong University of Science and Technology · The Chinese University of Hong Kong · SmartMore · Harbin Institute of Technology, Shenzhen · Shenzhen Loop Area Institute

Abstract

Reliable robotic manipulation requires timely intervention to correct emerging deviations and restore progress after execution errors. However, recovery methods based on repeated vision-language reasoning or iterative online optimization can incur substantial latency, delaying intervention. To address these challenges, we introduce RESETTLE(Robotic rEcovery through diSagrEement-Triggered reTrievaL and Efficient Corrective Control), a model-agnostic framework that provides computationally efficient recovery at the action-execution interface of frozen robot policies. RESETTLE triggers recovery when two action proposals independently sampled under identical conditioning persistently disagree. It retrieves a same-task demonstration reference using an adapted V-JEPA encoder and combines a state-servo prior with a guarded visual residual to execute one corrective action without online trajectory optimization or additional vision-language reasoning, then returns control to the base policy. Across six base policies in simulation, RESETTLE achieves up to 8.70%, 6.28%, and 6.83% absolute success-rate gains on LIBERO-Plus, Meta-World, and RoboCasa Tabletop, respectively, with further improvements on four real-world tasks using two policies. In QwenPI-based comparisons, its monitoring-and-recovery computation latency is 74.04%--93.57% lower than VoLoAgent's monitoring-and-planning latency for grasp and place tool calls. It also raises Harness VLA's LIBERO-Pro Swap success from 42% to 50%, demonstrating compatibility with high-level agentic planning. Code available at: https://github.com/JIA-Lab-research/RESETTLE

Figures & tables

Figure 1: QwenPI action disagreement on LIBERO-Plus for perturbations in layout (left) and robot-initialization (right) . Histograms show within-group decision percentages. “Training Data” denotes training-data inference; “Training P95” marks its 95th percentile. Failures exhibit heavier tails.

Figure 2: Overview of RESETTLE. Disagreement-Based Intervention module detects the execution error and initiates V-JEPA-Based Retrieval, followed by Reference-Guided Recovery using a state-servo prior and a guarded visual residual. The $a_{1},a_{2}$ are action chunks sampled with independent seeds; $O_{\mathrm{curr/ref}}$ and $S_{\mathrm{curr/ref}}$ denote current/reference observations and measured robot states. Each intervention executes one native control tick before re-observation and return to the base policy. ‘Snowflake’ signifies frozen policy while ‘flame’ refers to offline encoder adaptation.

Figure 3: Experimental settings across four simulation benchmarks and four real-world manipulation tasks using a Franka Research 3 robot with external and wrist-mounted cameras.

Model	Cam.	Robot	Lang.	Light	BG	Noise	Layout	Average
OpenVLA	0.8	3.5	23.0	8.1	34.8	15.2	28.5	15.6
OpenVLA-OFT	56.4	31.9	79.5	88.7	93.3	75.8	74.2	69.6
NORA	2.2	37.0	65.1	45.7	58.6	12.8	62.1	39.0
WorldVLA	0.1	27.9	41.6	43.7	17.1	10.9	38.0	25.0
UniVLA	1.8	46.2	69.6	69.0	81.0	21.2	31.9	42.9
$\pi_{0}$	13.8	6.0	58.8	85.0	81.4	79.0	68.8	53.6

Table 1: Results on LIBERO-Plus. Per-dimension success rates (%) on the LIBERO-Plus robustness benchmark. Cam.: camera; Lang.: language; BG: background; Robot: robot initialization. All models are trained on standard LIBERO and evaluated on the perturbed test set. Average aggregates all evaluation cases, weighting each dimension by its number of cases. “Ours” denotes RESETTLE, while Ours † denotes RESETTLE without the residual module.

Model	Easy (28)	Medium (11)	Hard (6)	Very Hard (5)	Average
QwenPI / Base.	80.36	57.27	45.00	60.00	60.66
QwenPI / Ours	83.57 $3.21\!\uparrow$	62.73 $5.46\!\uparrow$	51.67 $6.67\!\uparrow$	66.00 $6.00\!\uparrow$	65.99 $5.33\!\uparrow$
SmolVLA / Base.	79.64	41.82	83.33	32.00	59.20
SmolVLA / Ours	83.57 $3.93\!\uparrow$	46.36 $4.54\!\uparrow$	90.00 $6.67\!\uparrow$	42.00 $10.00\!\uparrow$	65.48 $6.28\!\uparrow$

Table 2: Success rates (%) across difficulty levels on Meta-World. Parentheses indicate the number of tasks in each difficulty group. “Ours” denotes RESETTLE. Arrows indicate absolute improvements (%) over the corresponding baseline.

Table 6

Model	Press-button	Cube-up	Carrot-in-pot	Use Spoon	Avg.
	SR	SR	SR	SC
QwenPI / Base.	90	55	85	36.7	66.67
QwenPI / Ours	100	70	95	50	78.75
VLAct / Base.	90	85	90	80	86.25
VLAct / Ours	100	90	95	86.7	92.92

Table 5: Real-world performance. Performance (%) over 20 trials per task and method. Press-button, Cube-up, and Carrot-in-pot report success rate (SR), while Use Spoon reports normalized stage completion (SC). “Ours” denotes RESETTLE. Avg. is the mean across four tasks.

Method	Monitor	Recovery	Total $\downarrow$	Relative latency $\downarrow$
RESETTLE (Ours)	$\approx$ 0.0	83.6	$\approx$ 83.6	$\mathbf{1.0}\times$
Agentic Robot	107.0	109.0	216.0	$2.6\times$
VoLoAgent	107.0	215.0–1193.0	322.0–1300.0	$3.9$ – $15.6\times$
LPB †	56.2	423.5	479.7	$5.7\times$

Table 6: Monitoring and recovery computation latency. Monitor, Recovery, and Total are in milliseconds; lower is better. Total is Monitor + Recovery under the measured QwenPI-based configurations. Monitor denotes additional latency beyond nominal policy inference. Relative latency is Total divided by RESETTLE’s 83.6 ms. VoLoAgent ranges give the place and grasp branches as lower and upper endpoints, respectively. LPB † is implemented in our QwenPI+V-JEPA framework.

Appendix figures & tables18 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 4: Paired grasp and contact recovery cases. QwenPI baseline and QwenPI+RESETTLE use the same task, layout, and seed in each pair. Panels (a)–(d) show grasp-pose correction, regrasping after a drop, recovery from an obstructed grasp, and correction during stove-knob operation. The upper yellow sequence is the baseline; the lower sequence alternates green base-policy execution with red RESETTLE interventions. Red frames labeled “VJEPA” denote the recovery controller using V-JEPA representations, reference retrieval, and guarded residual control. Frames progress from left to right.

Figure 5: Paired placement and grasp correction cases. Panels (a)–(c) show recovery during obstructed cup placement, correction after unintended object grasping, and grasp-pose adjustment that avoids an unfavorable dropped-object state. Each pair matches the task, layout, and seed, with yellow baseline frames above and green base-policy/red recovery frames below, following Figure 4 . Panel (c) includes recovery at two stages, interleaved with base-policy execution. Selected frames show local corrections and subsequent progress rather than every control step.

Figure 6: Effect of calibration set size on action disagreement. QwenPI distributions from 5, 100, and all training trajectories across seven LIBERO-Plus perturbation categories. The similar distribution shapes support lightweight threshold calibration using only five training trajectories.

Module	Total parameters	Trainable parameters
Context encoder	1,012,173,952	1,012,173,952
EMA target encoder	1,012,173,952	0
Action-conditioned predictor	305,220,992	305,213,824
Guarded residual-action head	6,839,226	6,839,226
All instantiated modules	2,336,408,122	1,324,227,002
Deployed subset	1,019,013,178	–

Appendix

Table 7: Parameter counts for the LIBERO recovery module. Trainable counts include only parameters optimized by backpropagation. The deployed subset consists of the context encoder and residual head; its trainable count is omitted because no optimization occurs during deployment. Counts exclude the base policy, reference-bank features, optimizer states, and non-parameter buffers.

Setting	LIBERO	RoboCasa	Meta-World
State/action dim.	7	29	4
Frames per clip	8	8	8
Input resolution	$256^{2}$	$256^{2}$	$256^{2}$
Per-GPU batch	16	16	16
Global batch	128	128	128
Training budget	20k updates	150k updates	20 epochs

Appendix

Table 8: Recovery-model training budgets. Training budgets for the benchmark-specific recovery models. Batch sizes count clips. A virtual epoch denotes a fixed number of optimizer updates rather than a complete dataset traversal.

Figure 7: Feature-distance and action-disagreement distributions across six LIBERO-Plus perturbation categories: language instructions, background textures, camera viewpoints, robot initial states, layout perturbations, and light conditions. Each pair shows V-JEPA distance to same-task training references (left, frame-level within-outcome percentages) and QwenPI action disagreement (right, within-group policy-decision percentages). Red and blue denote failed and successful executions; green in disagreement panels denotes offline training-data inference. $n$ counts frames or policy decisions in the respective panels, not rollouts. Vertical lines mark “Success P95” for feature distance and “Training P95” for action disagreement.

Perturbation	V-JEPA Feature Distance				Action Disagreement (Ours)
	Failed		Successful		Failed		Successful
	Rate (%)	Count	Rate (%)	Count	Rate (%)	Count	Rate (%)	Count
Camera	26.52	179.3	10.00	66.9	93.10	16.6	29.47	2.4
Robot	27.98	104.6	17.44	28.2	95.40	16.9	25.40	2.6
Language	15.48	70.5	17.89	24.8	77.89	9.6	9.22	1.9
Light	21.74	10.2	9.74	59.4	91.67	12.6	12.34	1.8

Appendix

Table 9: Failure coverage under two consecutive P95 exceedances. Rate: trajectories triggered (%). Count: mean triggers per triggered trajectory.

FastWAM / LIBERO-Plus
Setting	Failed		Successful
Setting	Rate (%)	Count	Rate (%)	Count
Camera	42.01	2.0	12.99	1.5
Robot	53.36	2.4	11.19	1.4
Language	68.99	3.5	8.91	1.4
Light	64.83	3.0	10.71	1.2

Appendix

Table 10: Action-disagreement triggers for additional policies and benchmarks, using training-data inference P95 thresholds and two consecutive exceedances with reset after each trigger. Rate: trajectories triggered (%). Count: mean triggers per triggered trajectory.

Perturbation	Mean takeovers per trajectory		Intervened trajectories (%)	Recovery actions (%)
Perturbation	All	Intervened	Intervened trajectories (%)	Recovery actions (%)
Camera	3.90	8.80	44.28	1.62
Robot	25.32	29.73	85.16	12.59
Language	0.07	2.78	2.34	0.04
Light	0.07	2.58	2.71	0.05
Background	0.29	2.70	10.78	0.19

Appendix

Table 11: Actual recovery usage for QwenPI+RESETTLE on LIBERO-Plus using a shared training-data inference P95 threshold and two consecutive exceedances. Each takeover executes one native recovery action. Mean counts include either all trajectories or only those with at least one takeover (Intervened). Recovery actions (%) denotes the share of all executed actions, excluding initialization waiting actions. Overall statistics are computed from pooled counts.

Setting	Cam.	Robot	Lang.	Light	BG	Noise	Layout	Total
Baseline	55.7	61.0	94.5	98.0	97.6	86.2	79.1	80.2
P85	58.7	84.2	94.7	97.5	97.7	79.5	78.8	83.1
P90	59.2	83.8	94.3	98.0	97.9	83.3	79.3	83.9
P95	59.9	82.4	94.7	97.8	98.6	86.9	80.1	84.6
P99	57.3	76.2	94.9	97.5	98.5	89.6	80.1	83.6

Appendix

Table 12: Threshold percentile ablation on LIBERO-Plus. Success rates (%) for QwenPI+RESETTLE using training-data inference thresholds and two consecutive exceedances. Baseline denotes QwenPI without RESETTLE, as reported in Table 1 . Total aggregates all evaluation cases, weighting each perturbation category by its number of cases. Cam.: camera; Lang.: language; BG: background; Robot: robot initialization. Values are rounded to one decimal place; bold marks the best result in each column, including ties.

Figure 8: Failures without a recovery trigger. Seven unsuccessful rollouts with no monitoring trigger, ordered from top to bottom. Frames progress left to right. The cases include contact-induced scene changes, misplaced objects, obstructed motions, and incorrect grasp or task decisions. Consistent policy proposals can therefore accompany unsuccessful execution.

Figure 9: Failures despite triggered recovery. Seven unsuccessful rollouts in which recovery is triggered, ordered from top to bottom, with frames progressing left to right. Cases illustrate contact-induced deterioration, unintended grasp configurations, execution-budget exhaustion, and task-goal mismatch. Triggered intervention does not guarantee completion within the evaluation budget.

Figure 10: Real-world training demonstrations. Rows show Carrot-in-pot, Press-button, Cube-up, and Use Spoon, from top to bottom, with time progressing from left to right. Selected frames illustrate object acquisition and placement, button contact, stacking, and tool use. Successful training demonstrations support recovery-module learning and provide same-task references for retrieval.

Figure 11: Real-world execution with QwenPI+RESETTLE. Task groups show Carrot-in-pot, Press-button, Cube-up, and Use Spoon from top to bottom. Frames progress left to right; Cube-up continues from its upper row to its lower row within one rollout. All sequences use QwenPI+RESETTLE, and individual recovery actions are not annotated. The selected frames show the combined system’s execution, including repeated approaches before cube acquisition and subsequent stacking.

ID	Task	QwenPI		SmolVLA
		Base.	Ours	Base.	Ours
00	nut-assembly	40	40	90	90
01	basketball	30	30	10	10
02	bin-picking	40	40	10	20
03	box-close	30	70	40	50
04	button-press-topdown	100	100	100	100

Appendix

Table 13: Per-task ASR on Meta-World (tasks 00–24). Success rates (%) over 10 episodes per task. Ours denotes RESETTLE; bold marks the better result within each model pair, including ties.

ID	Task	QwenPI		SmolVLA
		Base.	Ours	Base.	Ours
25	handle-pull-side	10	20	20	30
26	handle-pull	10	30	100	100
27	lever-pull	40	40	0	10
28	pick-place-wall	70	70	40	20
29	pick-out-of-hole	0	30	40	30

Appendix

Table 14: Per-task ASR on Meta-World (tasks 25–49). Success rates (%) over 10 episodes per task. Ours denotes RESETTLE; bold marks the better result within each model pair, including ties.

ID	Task	QwenGR00T		LDA
		Base.	Ours	Base.	Ours
01	Cup $\rightarrow$ Drawer (close)	36	34	50	42
02	Potato $\rightarrow$ Microwave (close)	30	42	42	36
03	Milk $\rightarrow$ Microwave (close)	60	56	40	50
04	Bottle $\rightarrow$ Cabinet (close)	78	76	70	78
05	Wine $\rightarrow$ Cabinet (close)	40	58	72	58

Appendix

Table 15: Per-task ASR on RoboCasa Tabletop / GR1. Success rates (%) over 50 episodes per task. Ours denotes RESETTLE; bold marks the better result within each model pair, including ties.

Task	Direct $\pi_{0.5}$		Harness VLA
	Base.	Ours	Base.	Ours
0	0	10	70	80
1	40	50	60	70
2	10	20	50	50
3	0	0	0	0
4	30	70	50	80

Appendix

Table 16: Per-task ASR on LIBERO-Pro Swap. Success rates (%) over 10 episodes per task. Ours denotes RESETTLE; bold marks the better result within each route, including ties.

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Robotic manipulation poses fundamental challenges due to uncertainty, long-horizon execution, and compounding errors, which can easily destabilize execution and lead to task failure. Although recent vision-language-action (VLA) models exhibit strong generalization, they typically lack explicit mechanisms to assess execution stability and to recover when execution deviates from its nominal behavior. In this paper, we propose: (1) two complementary metrics to assess execution quality at runtime, and (2) an agentic reinforcement learning framework that learns to restore effective execution through high-level decision-making rather than directly learning low-level actions. In this framework, an agentic policy reasons over recent execution history and selects among a small set of execution modes to regulate the execution process. Under execution degradation, it triggers appropriate recovery mechanisms to restore the robot to previously visited nominal states, enabling the task to continue. We evaluate the proposed method on the LIBERO benchmark, achieving up to a 13.7% improvement in success rate under standard settings and up to a 39.2% improvement under disturbance settings, demonstrating substantially enhanced execution robustness.

Abstract

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