RoboRecover: Benchmarking Robot Policy Recovery under Execution Deviations

Yang Li; Chen Zhao; Zhuoran Wang; Jiankang Wang; Chao Shao; Yihan Lin; Haitao Shen; Jing Zhang

cs.ROSep 24, 2026

RoboRecover: Benchmarking Robot Policy Recovery under Execution Deviations

Authors: Yang Li, Chen Zhao, Zhuoran Wang, Jiankang Wang, Chao Shao, Yihan Lin, Haitao Shen, Jing Zhang

Organizations: School of Information, Renmin University of China, Beijing, China · Key Laboratory of Data Engineering and Knowledge Engineering, Beijing, China · University of Science and Technology of China · Engineering Research Center of Database and Business Intelligence, Beijing, China

Abstract

Robot-policy benchmarks increasingly cover diverse tasks and preset out-of-distribution conditions, but typically evaluate complete trajectories from predefined initial states. These evaluations often focus on the initialized scene and the final outcome, while paying less attention to the dynamic interaction process. During closed-loop execution, actions and contacts can alter object relations and task progress, producing off-nominal intermediate states that need recovery. Recovery requires a policy to infer how task progress has changed, correct the relevant relations, and continue the original goal. We introduce RoboRecover, a benchmark for robot policy recovery under execution deviations. RoboRecover selects deviation states from trajectories, reconstructs them by replaying action prefixes, and evaluates policies on the original task. RoboRecover contains 2,000 scenarios across RoboTwin and LIBERO, with 1,000 scenarios and a fixed 800/200 train/test split on each platform. Results show that initial-state performance does not determine recovery performance and policies exhibit different recovery strengths across scenarios. Using its training split, RoboRecover further supports study on recovery interventions. RoboRecover establishes recovery from execution-induced intermediate states as a distinct dimension of robot policy evaluation.

Figures & tables

Figure 1: Initial-state evaluation vs. Recovery evaluation. Recovery evaluation selects the recovery point at which an execution deviation occurs during interaction.

Figure 2: RoboRecover benchmark composition and coverage. The upper panels summarize platform, split, task, construction source, and source policies. The lower matrix shows one representative recovery point for each of the nine Stage–Type groups; cell counts report RoboTwin/LIBERO test scenarios.

Figure 3: Policy performance changes from initial-state to recovery evaluation. Lines connect each policy’s rank under matched initial-state and recovery evaluation.

Figure 4: Recovery rates across Stage–Type groups. Cells include scenario counts, and sparse groups are visually de-emphasized. Differences between groups can be hidden by Overall RSR. A·SC: Approach-Scene Change.

Figure 5: Cross-policy recovery on Natural deviations. Rows identify source policies, and columns show the evaluated recovery policies. Solid outlines mark source-policy re-evaluation; dashed outlines mark the observed-best alternative.

Setting	Recovery procedure	RSR (%)
Direct $\pi_{0.5}$	Run $\pi_{0.5}$ from the recovery point	64.4
Reversion	Start 30 steps before the recovery point; run $\pi_{0.5}$	74.0
Monitor+Reversion	Run $\pi_{0.5}$ ; at the first trigger, rollback 30 steps and resume	77.0
Correction	Run Correction for 30 steps from the recovery point; resume $\pi_{0.5}$	71.2
Monitor+Correction	Run $\pi_{0.5}$ ; at the first trigger, apply Correction for 30 steps	75.5

Table 1: LIBERO recovery-intervention results. Reversion changes the startingstate, whereas Correction adds a 30-step learned controller. Monitor-basedsettings activate the corresponding intervention selectively.

Appendix figures & tables12 assets

Supplementary material from the paper’s appendix.

Appendix

Approach–Wrong Target Image pending Image pending RoboTwin LIBERO Redirect from an irrelevant object or region and approach the correct task target.	Approach–Wrong Pose Image pending Image pending RoboTwin LIBERO Correct the robot–object relation before attempting the intended interaction.	Approach–Stage Rollback Image pending Image pending RoboTwin LIBERO Recover lost task progress and return to a valid approach phase.
Approach–Scene Change Image pending Image pending RoboTwin LIBERO Reassess the changed scene and approach the currently valid object or region.	Contact–Wrong Pose Image pending Image pending RoboTwin LIBERO Restore effective contact or grasp from an unsuitable robot–object pose.	Move–Wrong Pose Image pending Image pending RoboTwin LIBERO Correct the object or end-effector pose before continuing transport.
Place–Wrong Target Image pending Image pending RoboTwin LIBERO Redirect the object from the wrong goal region to the instructed destination.	Place–Wrong Pose Image pending Image pending RoboTwin LIBERO Realign the object and robot before completing placement.	Place–Scene Change Image pending Image pending RoboTwin LIBERO Reassess the changed target configuration before completing placement.

Appendix

Figure 6: Representative recovery observations for the nine occupied Stage–Type groups. Each panel pairs RoboTwin (left) and LIBERO (right) examples and summarizes the recovery required to continue the original task.

Platform	Split	Scen.	Tasks	Natural	Pert.	Human
RoboTwin	Train	800	42	800	–	–
	Test	200	34	200	–	–
LIBERO	Train	800	40	170	504	126
	Test	200	38	42	127	31

Appendix

Table 3: Corpus composition. Pert. denotes Action-Perturbed scenarios; dashes indicate construction sources not used on RoboTwin.

RoboTwin
Policy	Initial-state [95% CI]	Recovery [95% CI]
X-VLA	54.33 [47.60,60.86]	49.83 [43.25,56.35]
LingBot-VLA	64.33 [57.62,70.65]	43.20 [36.60,49.57]
SmolVLA	42.67 [37.11,48.13]	33.03 [27.66,38.65]
$\pi_{0.5}$	34.50 [29.81,39.27]	32.60 [27.48,37.83]
Fast-WAM	81.00 [75.00,86.63]	49.33 [42.12,56.46]

Appendix

Table 4: Complete initial-state and recovery results on the fixed test splits (%). Bold marks the highest point estimate in each platform and evaluation condition.

RoboTwin policy	Macro-9	LIBERO policy	Macro-9
X-VLA	40.65	$\pi_{0}$	36.79
LingBot-VLA	41.30	$\pi_{0.5}$	67.71
SmolVLA	33.52	Being-H0.5	48.68
$\pi_{0.5}$	34.33	UniFOLM	50.26
Fast-WAM	55.62	Cosmos-Policy	50.56
LingBot-VA	53.19	Fast-WAM	54.01

Appendix

Table 5: Macro-9 RSR on the fixed test splits (%), computed from unrounded scenario-level rates. Best values are bold.

RoboTwin
Pair $A-B$	Initial-state $\Delta$ (95% CI)	Recovery $\Delta$ (95% CI)
$X-L$	-10.00 [-19.24,-0.51]	6.63 [-3.45,16.73]
$X-S$	11.67 [3.85,19.50]	16.80 [8.38,25.08]
$X-P$	19.83 [11.68,27.81]	17.23 [8.16,26.21]
$X-F$	-26.67 [-34.97,-18.26]	0.50 [-9.02,10.23]
$X-V$	-29.42 [-37.35,-21.49]	-9.67 [-18.77,-0.84]

Appendix

Table 6: All paired policy contrasts in percentage points. RoboTwin intervals use trajectory-cluster bootstrap; LIBERO intervals use paired scenario bootstrap.

Model	LIBERO-Plus Total	RoboRecover Initial-state	RoboRecover RSR
$\pi_{0}$	53.6 (69.4 rerun)	85.17	37.80
$\pi_{0.5}$	85.7	94.17	64.40
Being-H0.5	78.5 †	91.67	46.80
UniFOLM	–	98.83	48.00
Cosmos-Policy	82.2	97.83	52.10
Fast-WAM	51.5	96.83	54.00

Appendix

Table 7: Published LIBERO-Plus results and RoboRecover results for overlapping model families (%). The

\pi_{0}

value in parentheses is the JAX rerun reported by Zhang et al. (2026) . † Being-H0.5 is reported by Luo et al. (2026b) . ‡ SmolVLA is reported under a separate LIBERO training and evaluation setup by Li et al. (2026a) . Dashes indicate that no directly reported aggregate LIBERO-Plus result was identified.

RoboTwin Stage–Type ( $N$ )	X-VLA	LingBot-VLA	SmolVLA
Approach–Scene Change (4)	8.33	33.33	0.00
Approach–Stage Rollback (24)	46.67	26.39	16.11
Approach–Wrong Pose (44)	66.36	41.06	30.91
Approach–Wrong Target (29)	49.43	38.62	16.09
Contact–Wrong Pose (21)	43.81	65.71	35.87
Move–Wrong Pose (35)	53.52	33.33	46.10

Appendix

Table 8: Complete RoboTwin recovery rates across Stage–Type groups (%). The two Scene Change groups contain only four and two scenarios.

LIBERO Stage–Type ( $N$ )	$\pi_{0}$	$\pi_{0.5}$	Being-H0.5
Approach–Scene Change (28)	12.1	50.0	24.3
Approach–Stage Rollback (21)	39.0	77.1	35.2
Approach–Wrong Pose (49)	42.4	59.6	42.4
Approach–Wrong Target (25)	40.8	70.4	58.4
Contact–Wrong Pose (12)	30.0	55.0	48.3
Move–Wrong Pose (16)	53.8	71.2	63.7

Appendix

Table 9: Complete LIBERO recovery rates across Stage–Type groups (%). Cosmos denotes Cosmos-Policy. The Place–Wrong Target group contains seven scenarios and is reported descriptively.

Group ( $N$ )	$\pi_{0}$	$\pi_{0.5}$	Being-H0.5
Construction source
Human-constructed (31)	44.5	70.3	38.7
Natural (42)	48.6	58.1	55.7
Action-perturbed (127)	32.6	65.0	45.8
Task suite
LIBERO-Spatial (28)	40.7	74.3	39.3

Appendix

Table 10: LIBERO recovery rates by construction source and task suite (%).

RoboTwin source ( $N$ )	X-VLA	LingBot-VLA	SmolVLA
X-VLA (24)	17.78	42.22	58.06
LingBot-VLA (37)	59.10	19.82	48.83
SmolVLA (83)	43.21	57.67	17.27
$\pi_{0.5}$ (56)	67.26	37.62	35.24

Appendix

Table 11: Complete Natural source-policy by recovery-policy matrices (%). Cosmos denotes Cosmos-Policy. Rows with small

N

are descriptive.

Split	IDs	Frames	Label 0	Label 1
Training	720	320,000	160,000	160,000
Balanced val.	80	8,000	4,000	4,000
Natural val.	80	16,000	5,831	10,169

Appendix

Table 12: RoboTwin monitor training and validation data. The two validation sets use different frame-sampling distributions from the same 80 held-out scenario IDs.

Validation	Acc.	Prec.	Recall	F1	Macro-F1	FPR
Balanced	85.49	86.11	84.63	85.36	85.49	13.65
Natural	86.42	91.99	86.14	88.97	85.66	13.09

Appendix

Table 13: RoboTwin monitor validation results (%). The lower panel gives the corresponding confusion matrices. FPR is the false-positive rate.

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Vision-Language-Action (VLA) or World Action (WAM) models have recently demonstrated remarkable performance in robotic manipulation. On LIBERO, SOTA method have achieved nearly 100% success rates, seemingly suggesting that the models are ready for deployment in real world. However, near perfect performance on existing benchmarks can be misleading: success under ideal conditions does not imply real world robustness. Existing benchmarks primarily evaluate task completion from predefined initial states, while real world interactions inevitably involve failures such as failed grasps, collisions, and unintended object movements. A robot must therefore not only execute tasks successfully, but also recognize and recover from failures to continue the task. Yet this capability remains largely unmeasured, revealing a critical gap between benchmark performance and real world reliability. To address this gap, we introduce LIBERO-Recover Benchmark, a large scale benchmark for failure recovery in robotic manipulation. Built upon LIBERO, we collect real execution failures from SOTA embodied models and construct 1,000+ scenarios across four recovery levels: (1) Action Retry, (2) Action Adaptation, (3) Object State Recovery, and (4) Environmental Recovery. We evaluate four core capabilities: spatial understanding, object structure reasoning, interaction understanding, and topological reasoning. As the first large-scale benchmark for embodied failure recovery, LIBERO-Recover shifts evaluation from \emph{Can the robot succeed?''} to \emph{Can the robot recover after failure?''}, promoting robust and generalizable embodied agents. The project will be avaible in \textcolor{blue}{https://liulin815.github.io/LIBERO-Recovery/}.

Abstract

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