RoboAuto: Autonomous Robot Program Search through Multi-Level Physical Understanding

Buzhao Liu; Jianing Ye; Xiu Yuan; Yangtong Feng; Chengsong Huang; Tyler Dinh; Yevgeniy Vorobeychik; Ning Zhang

cs.ROOct 27, 2026

RoboAuto: Autonomous Robot Program Search through Multi-Level Physical Understanding

Authors: Buzhao Liu, Jianing Ye, Xiu Yuan, Yangtong Feng, Chengsong Huang, Tyler Dinh, Yevgeniy Vorobeychik, Ning Zhang,

Organizations: Washington University in St. Louis

Abstract

Recently, LLM-based autonomous agents have shown strong capabilities in iteratively generating, evaluating, and refining executable solutions. In robotic manipulation, Code-as-Policy lets these agents search over modular and editable robot programs. However, effective program search requires physical understanding: agents must understand how objects can be manipulated, how scene geometry constrains actions, and why interactions succeed or fail. Existing methods rely mainly on execution feedback for program refinement, often leaving these physical questions unresolved. We introduce RoboAuto, a framework for robot program search that explicitly organizes physical understanding at the object, scene, and interaction levels through three components. First, a hierarchical knowledge base combines executable skills with object-centric knowledge of affordances, spatial relations, and action effects, learned from embodied data and online task solving. Second, scene grounding connects this knowledge to the current environment by identifying relevant objects and resolving action-critical quantities. Third, multi-stage planning with self-designed experiments checks whether each stage produces its expected physical effects and actively tests competing hypotheses to resolve physical uncertainties. RoboAuto achieves superior performance across five benchmark families, attaining an 89.1% success rate on short-horizon LIBERO-PRO tasks and more than twice the mean success rate of ASPIRE on LIBERO-PRO Long and LIBERO-Plus. It also outperforms ASPIRE and $π_{0.5}$ on three challenging long-horizon real-robot tasks requiring precise manipulation. We further show that the knowledge base transfers across simulators and embodiments, and that program search performance scales with its size. Project page: https://roboauto-ai.github.io/

Figures & tables

Figure 2: Overview of RoboAuto. The Hierarchical Knowledge Base (left) stores object-centric physical knowledge, comprising affordances, spatial relations, and action effects, together with executable skills. Physical Scene Grounding (middle) binds the entries relevant to a task to quantities measured in the current scene. Search and Exploration (right) plans the task as stages that end in verified state changes and designs experiments that separate competing physical hypotheses. Interaction evidence updates the knowledge, and verified programs return to the knowledge base as new skills. RoboAuto is evaluated across five benchmark families, LIBERO-PRO, LIBERO-PRO Long, LIBERO-Plus, Robosuite, and RoboTwin 2.0, where RoboTwin 2.0 tests transfer to a new simulator and embodiment.

Figure 3: Unresolved physical assumptions in our baseline runs. In the illustrated ASPIRE and CaP-Agent0 runs, (a) an unsuitable contact strategy persists, (b) a grasp-height rule is transferred without grounding its geometric applicability, and (c) pan placement proceeds without verifying stove activation. The panels show the attempts, outcomes, and retained program choices; Appendix D provides the detailed case studies.

Figure 4: Program success across benchmarks. (a–c) Each dot is a LIBERO-PRO task under position or task perturbation. (d–f) Horizontal bars give each method’s success on every LIBERO-PRO Long, LIBERO-Plus, and Robosuite setting, ordered from the top by RoboAuto’s success, which is printed beside its bar. Dashed lines and their labels give method means.

Figure 5: Ablations of the three components. Mean final-program success over five tasks as Search and Exploration, the Hierarchical Knowledge Base, and Physical Scene Grounding are cumulatively added to the base loop.

Figure 6: Hierarchical knowledge base transfer to RoboTwin 2.0. Success on four RoboTwin 2.0 tasks without retained knowledge and with the hierarchical knowledge base learned from LIBERO. Dashed lines indicate mean success.

Figure 7: Knowledge availability and program search. Mean test success versus search rollouts on the five ablation tasks with 0%, 50%, and 100% of the knowledge base. The 0% and 100% conditions are “+ Search & Exploration” and RoboAuto in Fig. 5 .

Figure 8: Real-robot rollouts. RoboAuto programs on a WidowX AI arm for three tasks. Each row shows four chronological scene-camera keyframes.

Task	RoboAuto	ASPIRE	$\pi_{0.5}$
Sort Balls	100.00%	30.00%	0.00%
Stack Dishes	100.00%	3.75%	0.00%
Erase Board	95.83%	0.00%	0.00%

Table 1: Real-robot evaluation. Mean stage-completion score over 20 trials per task. Figure 8 shows example rollouts.

Appendix figures & tables29 assets

Supplementary material from the paper’s appendix.

Appendix

Suite	Settings	Training seeds	Test seeds	Rollouts
LIBERO-PRO Object	20	110000–110014	1–50	45
LIBERO-PRO Goal	20	110000–110014	1–50	45
LIBERO-PRO Spatial	20	110000–110014	1–50	45
LIBERO-PRO Long	10	110000–110014	1–50	55
LIBERO-Plus Spatial	2	110000–110014	1–50	45
LIBERO-Plus Goal	2	110000–110014	1–50	45

Appendix

Table 2: Scene seeds and rollout budgets. Seed intervals include both endpoints. The number of settings counts task–perturbation combinations.

Same scene layout, same objects
Benchmark	#Set.	RoboAuto	ASPIRE	CaP- Agent0	RHO	$\pi_{0.5}$
LIBERO-PRO Long	10	73.4	30.8	8.6	–	0.4
LIBERO-Plus	2	80.0	20.0	1.0	–	49.0
All	12	74.5	29.0	7.3	–	8.5
Different scene layout, same objects
LIBERO-PRO Goal	17	77.4	74.0	24.0	50.9	23.1

Appendix

Table 3: Results by similarity to the LIBERO-90 demonstrations. Mean success rate (%) across evaluation settings, grouped by whether their scene layout and manipulated object set match those in the LIBERO-90 demonstrations used for knowledge construction. #Set. denotes the number of evaluation settings in each row.

	LIBERO-PRO (60)						LIBERO-PRO Long (10)			LIBERO-Plus (6)			Robosuite
Method	Object	Goal	Spatial	Pos	Task	All	Pos	Task	All	Camera	Layout	All	(7)
$\pi_{0.5}$ [ 19 ]	9.1	19.9	10.6	25.2	1.2	13.2	0.3	0.5	0.4	38.0	98.0	68.0	–
CaP-Agent0 [ 13 ]	20.0	21.2	12.9	19.7	16.3	18.0	9.3	7.5	8.6	24.0	42.7	33.3	68.3
RHO [ 11 ]	24.7	53.1	57.3	38.6	51.5	45.0	–	–	–	–	–	–	70.0
ASPIRE [ 25 ]	96.6	63.1	55.3	76.5	66.8	71.7	26.7	37.0	30.8	46.0	43.3	44.7	81.0
RoboAuto (ours)	98.7	73.7	94.8	90.2	87.9	89.1	60.0	93.5	73.4	84.7	96.0	90.3	90.6

Appendix

Table 4: Mean success (%) by benchmark and perturbation. Each mean weights task–perturbation settings equally; setting counts are given under each column group. A dash denotes an unavailable result. The last row gives RoboAuto’s difference from ASPIRE. Per-task results follow in Tables 6 , 7 , 8 , 9 , 10 and 11 .

Suite	RoboAuto	ASPIRE	CaP-Agent0	RHO	$\pi_{0.5}$
Object	98.7	96.6	20.0	24.7	9.1
Goal	73.7	63.1	21.2	53.1	19.9
Spatial	94.8	55.3	12.9	57.3	10.6
All settings	89.1	71.7	18.0	45.0	13.2

Appendix

Table 5: LIBERO-PRO suite means. Equal-weight mean success (%) across position and task perturbations.

		RoboAuto		ASPIRE		CaP-Agent0		RHO		$\pi_{0.5}$
ID	Task	Pos	Task	Pos	Task	Pos	Task	Pos	Task	Pos	Task
1	Place(alphabet_soup, basket)	100	100	100	100	2	4	12	40	0	0
2	Place(bbq_sauce, basket)	92	98	100	100	12	42	32	88	100	2
3	Place(butter, basket)	98	100	100	98	26	18	12	98	54	0
4	Place(chocolate_pudding, basket)	100	96	100	100	18	48	0	0	0	2
5	Place(cream_cheese, basket)	98	98	100	72	12	6	0	0	0	0

Appendix

Table 6: LIBERO-PRO Object. Reported success (%) under position (Pos) and task (Task) perturbations; see Table 2 for RoboAuto’s seed protocol and search budget.

		RoboAuto		ASPIRE		CaP-Agent0		RHO		$\pi_{0.5}$
ID	Task	Pos	Task	Pos	Task	Pos	Task	Pos	Task	Pos	Task
1	Open(cabinet, drawer_mid)	100	4	72	0	0	0	0	0	0	4
2	Put(bowl, drawer_top)	0	26	86	46	4	0	30	32	94	2
3	Push(plate, stove_front)	0	6	0	76	0	10	0	0	0	0
4	Put(bowl, plate)	100	100	92	88	36	38	98	98	0	2
5	Put(bowl, stove)	98	98	96	0	22	12	100	98	0	4

Appendix

Table 7: LIBERO-PRO Goal. Reported success (%) under position (Pos) and task (Task) perturbations; see Table 2 for RoboAuto’s seed protocol and search budget.

		RoboAuto		ASPIRE		CaP-Agent0		RHO		$\pi_{0.5}$
ID	Task	Pos	Task	Pos	Task	Pos	Task	Pos	Task	Pos	Task
1	Pick(between(plate, ramekin), plate)	92	100	94	8	22	14	92	60	2	0
2	Pick(table_center, plate)	98	78	32	64	22	14	96	96	0	2
3	Pick(drawer_top(cabinet_wood), plate)	78	92	10	96	2	10	6	4	0	0
4	Pick(next_to(cookie_box), plate)	100	98	16	94	0	10	0	28	0	2
5	Pick(next_to(plate), plate)	78	98	100	70	10	20	56	64	0	0

Appendix

Table 8: LIBERO-PRO Spatial. Reported success (%) under position (Pos) and task (Task) perturbations; see Table 2 for RoboAuto’s seed protocol and search budget.

ID	Task	Perturbation	RoboAuto	ASPIRE	CaP-Agent0	$\pi_{0.5}$
1	Place(alphabet_soup, basket) + Place(cream_cheese, basket)	Pos	100	46	40	0
2	Put(white_mug, plate_left) + Put(yellow_white_mug, plate_right)	Task	100	60	12	2
3	Put(white_mug, plate) + Put(chocolate_pudding, right_of(plate))	Task	94	20	10	0
4	TurnOn(stove) + Put(moka_pot, stove)	Task	92	0	8	0
5	Put(book, caddy_back)	Pos	74	26	0	0
6	Place(alphabet_soup, basket) + Place(tomato_sauce, basket)	Pos	62	10	16	2

Appendix

Table 9: LIBERO-PRO Long. Reported success (%); the seed protocol is in Table 2 .

ID	Task	Perturbation	Level	RoboAuto	ASPIRE	CaP-Agent0	$\pi_{0.5}$
1	Pick(next_to(ramekin), plate)	Camera	5	88	10	46	10
2	Pick(next_to(ramekin), plate)	Layout	5	94	18	60	100
3	Put(white_mug, plate) + Put(chocolate_pudding, right_of(plate))	Camera	5	66	28	0	4
4	Put(white_mug, plate) + Put(chocolate_pudding, right_of(plate))	Layout	5	94	12	2	94
5	TurnOn(stove)	Camera	4	100	100	26	100
6	TurnOn(stove)	Layout	5	100	100	66	100

Appendix

Table 10: LIBERO-Plus. Reported success (%); the seed protocol is in Table 2 .

ID	Task	RoboAuto	ASPIRE	CaP- Agent0	RHO
1	cube_lift	100	97	97	98
2	cube_stack	98	99	98	98
3	cube_restack	100	100	89	54
4	spill_wipe	97	99	100	100
5	two_arm_handover	75	92	20	86
6	two_arm_lift	90	71	74	53

Appendix

Table 11: Robosuite. Reported success (%) on seven tasks; see Table 2 for seeds and budget.

Suite	Task	Perturbation	Base loop	+ Search & Exploration	+ KB	RoboAuto
Long	TurnOn(stove) + Put(moka_pot, stove)	Task	8	82	84	92
Long	Put(white_mug, plate) + Put(chocolate_pudding, right_of(plate))	Task	4	4	78	94
Spatial ( LIBERO-Plus )	Pick(next_to(ramekin), plate)	Layout	14	10	30	94
Spatial	Pick(drawer_top(cabinet_wood), plate)	Pos	0	0	0	78
Goal	Put(bowl, stove)	Task	0	0	0	98
Mean			5.2	19.2	38.4	91.2

Appendix

Table 12: Component ablation. Reported success (%). KB denotes the hierarchical knowledge base. Each task uses the seeds and budget of its suite in Table 2 .

Figure 9: Per-task ablation trajectories. Test success versus the number of search rollouts as Search and Exploration, the knowledge base (KB), and scene grounding are added cumulatively to the base loop.

Suite	Task	Perturbation	ASPIRE	ASPIRE + KB	RoboAuto
Spatial	Pick(between(plate, ramekin), plate)	Task	8	40	100
Spatial	Pick(next_to(cookie_box), plate)	Pos	16	40	100
Spatial	Pick(next_to(ramekin), plate)	Pos	20	38	98
Spatial	Pick(on(cookie_box), plate)	Pos	24	18	92
Goal	Put(bowl, stove)	Task	0	0	98
Goal	Open(cabinet, drawer_mid)	Task	0	0	4

Appendix

Table 13: Hierarchical knowledge base with ASPIRE. Reported success (%). KB denotes the hierarchical knowledge base. Each task uses the seeds and budget of its suite in Table 2 .

ID	Task	No retained knowledge	Hierarchical knowledge base
1	Stack two bowls	2	88
2	Open microwave	0	96
3	Container on plate	40	92
4	Place empty cup	56	90
Mean		24.5	91.5

Appendix

Table 14: RoboTwin 2.0. Reported success (%) with an 11-rollout search budget and the seed protocol in Table 2 .

Suite	Task	Perturbation	0% KB	50% KB	100% KB
Long	TurnOn(stove) + Put(moka_pot, stove)	Task	82	86	92
Long	Put(white_mug, plate) + Put(chocolate_pudding, right_of(plate))	Task	4	86	94
Spatial ( LIBERO-Plus )	Pick(next_to(ramekin), plate)	Layout	10	98	94
Spatial	Pick(drawer_top(cabinet_wood), plate)	Pos	0	56	78
Goal	Put(bowl, stove)	Task	0	0	98
Mean			19.2	65.2	91.2

Appendix

Table 15: Knowledge-base size. Reported success (%). KB denotes the hierarchical knowledge base. Each task uses the seeds and budget of its suite in Table 2 .

Figure 10: Per-task knowledge-availability trajectories. Test success versus the number of search rollouts with 0%, randomly sampled 50%, and 100% of the knowledge base entries.

Suite	Task	Perturbation	Base loop	RoboAuto
Goal	Open(cabinet, drawer_mid)	Task	0	36
Goal	Push(plate, stove_front)	Pos	12	74
Spatial	Pick(drawer_top(cabinet_wood), plate)	Pos	78	98
Long	Put(white_mug, plate) + Put(chocolate_pudding, right_of(plate))	Pos	58	84
Long	Put(moka_pot_1, stove) + Put(moka_pot_2, stove)	Pos	0	96
Mean			29.6	77.6

Appendix

Table 16: Stronger-model comparison. Reported success (%). Each task uses the seeds and budget of its suite in Table 2 .

Task / perturbation	Run 1	Run 2	Run 3	Mean	Sample s.d.
Mug + pudding, task perturbed	94	92	88	91.3	3.1
Pick(next_to(ramekin), plate), camera perturbed	88	92	86	88.7	3.1
Place(chocolate_pudding, basket), task perturbed	96	100	92	96.0	4.0

Appendix

Table 17: Repeated search runs. Each final program is evaluated on test scenes (seeds 1–50). Mean and sample standard deviation are in percentage points.

Figure 11: Token usage and program success on matched tasks. Geometric-mean total tokens per task and mean success on LIBERO-PRO Long and LIBERO-Plus settings with complete token records for RoboAuto, ASPIRE, and CaP-Agent0.

		No retained knowledge		Hierarchical knowledge base
ID	Task	Output (M) $\downarrow$	Total (M) $\downarrow$	Output (M) $\downarrow$	Total (M) $\downarrow$
1	Stack two bowls	0.190	8.780	0.210	18.620
2	Open microwave	0.280	13.710	0.420	33.320
3	Container on plate	0.180	8.580	0.280	29.890
4	Place empty cup	0.080	6.170	0.120	12.640
Mean		0.183	9.310	0.258	23.618

Appendix

Table 18: RoboTwin 2.0 token usage. Output and total counts are in millions. Task IDs correspond to Table 14 .

ID	Task	Perturbation	Output (M) $\downarrow$	Total (M) $\downarrow$	Output (M) $\downarrow$	Total (M) $\downarrow$	Output (M) $\downarrow$	Total (M) $\downarrow$
			RoboAuto		ASPIRE		CaP-Agent0
1	Place(alphabet_soup, basket) + Place(cream_cheese, basket)	Pos	0.200	15.700	0.240	39.080	25.500	176.200
2	Put(white_mug, plate_left) + Put(yellow_white_mug, plate_right)	Task	1.160	89.270	0.763	2099.000	34.900	254.800
3	Put(white_mug, plate) + Put(chocolate_pudding, right_of(plate))	Task	0.610	26.030	0.420	162.430	51.600	276.100
4	TurnOn(stove) + Put(moka_pot, stove)	Task	0.390	28.490	0.690	300.850	35.800	443.700
5	Put(book, caddy_back)	Pos	0.520	34.880	0.300	171.080	40.600	465.300

Appendix

Table 19: LIBERO-PRO Long token usage. Output and total counts are in millions. Task IDs correspond to Table 9 .

			RoboAuto		ASPIRE		CaP-Agent0
ID	Task	Perturbation	Output (M) $\downarrow$	Total (M) $\downarrow$	Output (M) $\downarrow$	Total (M) $\downarrow$	Output (M) $\downarrow$	Total (M) $\downarrow$
1	Pick(next_to(ramekin), plate)	Camera	0.590	26.200	0.313	94.480	4.630	59.140
2	Pick(next_to(ramekin), plate)	Layout	0.420	39.920	0.509	624.260	2.260	33.280
3	Put(white_mug, plate) + Put(chocolate_pudding, right_of(plate))	Camera	0.570	38.500	0.137	106.450	9.910	95.580
4	Put(white_mug, plate) + Put(chocolate_pudding, right_of(plate))	Layout	1.530	70.240	0.156	34.180	9.710	91.310
5	TurnOn(stove)	Camera	0.160	8.640	0.152	29.210	3.310	44.900

Appendix

Table 20: LIBERO-Plus token usage. Output and total counts are in millions. Task IDs correspond to Table 10 .

Figure 12: Constructing object-centric knowledge from embodied data. A vision-language model converts each demonstration video into an evidence record containing entities, temporally localized events, and an episode outcome. The example shows the LIBERO-90 task “close the microwave,” in which event E06 moves the door from open to flush. Knowledge distilled across such records is organized into affordances, spatial relations, and action effects. The expanded affordance entry cites supporting events from multiple episodes and declares the scene-dependent quantities, shown in teal, that must be measured again when the entry is applied in a new scene.

Figure 13: Search and Exploration. The coder decomposes the task into stages with intended object states and executable verifiers (1), and the environment executes these stages sequentially (2). When an execution fails, an independent auditor evaluates the stage outcomes from synchronized videos and may reject a state that the program verifier accepted (3). The earliest incomplete stage is returned to the coder (4), which revises the stage or replans the task before executing the next candidate program (5). If the cause remains uncertain, the coder branches into a separately implemented experiment that collects targeted physical evidence before revision continues. Programs verified across the search pool contribute reusable executable skills.

Figure 14: The four component variants on turning on the stove and placing the pan (LIBERO-PRO Long, task perturbation of turning on the stove and placing the moka pot). Each panel adds one component to the preceding variant. The base loop rewrites the complete program from a per-scene reward. Search and Exploration plans stages whose verdicts localize the failure. The Hierarchical Knowledge Base supplies knob and handle contacts in the first turn but estimates their quantities from segmentation-mask spread and fixed offsets. Physical Scene Grounding measures the knob height and pan geometry and verifies burner activation before the pan is moved.

Figure 15: Book into the caddy (LIBERO-PRO Long, position perturbation). CaP-Agent0 continues using top-down grasps after its first approach topples the book. RoboAuto selects a cover pinch from the thin-slab entry and grounds the quantities required to execute it.

Figure 16: Placing the red mug on the plate and the pudding to its right (LIBERO-PRO Long, task perturbation of placing the white mug on the plate and the pudding to its right). ASPIRE applies a grasp-height rule stored in a skill learned on a different mug, causing the gripper to close on air in approximately half of the training scenes. RoboAuto measures the rim of the current mug and places the pads on it.

Figure 17: Turning on the stove and placing the pan (LIBERO-PRO Long, task perturbation of turning on the stove and placing the moka pot). CaP-Agent0 rotates the knob by a stored angle and proceeds without checking the burner. RoboAuto’s knob entry specifies the expected observable effect, which the program verifies before beginning pan placement.

Figure 18: Real-robot rollouts in detail. RoboAuto programs on a WidowX AI arm for three tasks. Each row shows nine scene-camera keyframes in time order and one wrist-camera frame. Orange labels mark actions in progress, and checked labels mark stages verified before the next stage begins.

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ASPIRE: Agentic /Skills Discovery for Robotics

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Traditional robot programming is notoriously challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse environment configurations and execution failures. We introduce ASPIRE (Agentic Skill Programming through Iterative Robot Exploration), a continual learning system that autonomously writes and refines robot control programs in a code-as-policy paradigm while compounding experience into a reusable skill library. ASPIRE operates in an open-ended learning loop, consisting of three components: (1) a closed-loop robot execution engine that exposes fine-grained multimodal traces, enabling the agent to autonomously diagnose failures, synthesize repairs, and validate outcomes; (2) a continually expanding skill library that distills validated fixes into reusable, transferable robotic knowledge; and (3) an evolutionary search procedure that generates diverse programs, systematically debugging them to explore beyond single-trajectory refinement. ASPIRE surpasses prior methods by up to 77% on manipulation tasks under perturbation, 72% on Robosuite's bimanual handover task, and up to 32% on long-horizon household tasks. The accumulated skill library further enables zero-shot generalization: on representative unseen long-horizon tasks, ASPIRE achieves 31% success, outperforming the 4% success rate of prior methods. Performance scales with the capabilities of frontier models. With Claude Opus 5.5, ASPIRE nearly saturates these benchmarks, achieving 99% average success across all 80 LIBERO-Pro tasks and 100% success on representative Robosuite and BEHAVIOR-1K tasks. Beyond simulation, skills discovered in simulation reduce real-world robot programming effort across embodiments and robot APIs, while skills acquired through online interaction with physical robots enable ASPIRE to solve a Rubik's Cube with progressively improving efficiency.

Abstract

Large Language Model-Based Robot Planning

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