RoboFoundry: System-as-Policy Evolution for Self-Learning Embodied Agents

Jingsong Liang; Shuhao Liao; Shizhe Zhang; Diyuan Hou; Yuxin Cai; Xinjian Deng; Chengyang He; Wenhui Huang

cs.ROSep 26, 2026

RoboFoundry: System-as-Policy Evolution for Self-Learning Embodied Agents

Authors: Jingsong Liang, Shuhao Liao, Shizhe Zhang, Diyuan Hou, Yuxin Cai, Xinjian Deng, Chengyang He, Wenhui Huang,

Organizations: Nanyang Technological University · Beihang University · National University of Singapore · Cloud Butterfly Technology ·

Abstract

A foundation model should not act in isolation as an embodied agent. Yet, existing methods often optimize individual components of the agent stack, such as memory, context, skills, or action interfaces, rather than treating the supporting system itself as a unified policy. Moreover, interaction alone does not yield self-improvement unless execution experience is converted into persistent, validated system changes. We therefore propose RoboFoundry, the first embodied agentic framework that formulates this process as Self-Evolving System-as-Policy. RoboFoundry diagnoses capability gaps in decision-making and memory management, converts execution traces into validated task-specific system updates, and promotes recurring improvements to the general system. Evolution operates over two complementary surfaces: a context system that manages active internal context and persistent file-system memory, and a hierarchical skill system that organizes atomic skills, reusable compositions, and failure-conditioned recovery. A shared semantic interface separates embodiment-invariant decisions from embodiment-specific execution, allowing evolved system capabilities to transfer across heterogeneous robots. On EmbodiedBench, RoboFoundry achieves state-of-the-art performance, notably improving GPT-5.5 by 27.8%. It also brings Qwen3.7-Plus to near parity with GPT-5.5 (70.3% vs. 72.7%), showing consistent gains from system-as-policy evolution across foundation models. For long-horizon memory, RoboFoundry outperforms all baselines on RoboMemArena by at least 39.0%, even against methods assisted by external foundation models. On LIBERO-PRO, it further outperforms Cap-Agent0 by 243.8%-679.7% across all perturbation types. In real-world deployments, RoboFoundry demonstrates zero-shot transfer and online evolution across robots and tasks, highlighting its potential for fully autonomous embodied agents.

Figures & tables

Figure 1: RoboFoundry: System-as-Policy Evolution across benchmarks and real robots. The central Act–Reflect–Repair–Promote loop executes across embodiments, attributes failures to capabilities, revises the task-level system, and promotes validated changes to the general system. The surrounding panels show gains across foundation models on EmbodiedBench, long-horizon memory results on RoboMemArena, and robustness perturbations under LIBERO-PRO. The capability plots compare GPT-5.5 and Qwen3.7-Plus before and after RoboFoundry, while the lower image strip illustrates adaptation and evolution on physical robots.

Figure 2: Overview of RoboFoundry. (a) The inner loop runs task-specific System-as-Policy through context/skill surfaces and semantic/execution bindings, producing embodiment-specific rollouts and traces. (b) The general system supplies reusable context and skill rules to task-level systems. (c) The outer loop diagnoses capability failures and validates targeted edits through held-in repair and held-out retention. Accepted edits update the task-specific system or promote the general system.

Figure 3: Example of evolution trace on the EB-Habitat spatial subset. Here, RoboFoundry (GPT-5.5) rises Held-in success rate from $21.4\text{\,}\%$ to $78.0\text{\,}\%$ over six candidate evaluations. The final version passes the held-out check and is promoted to the general system.

Figure 4: Example of scaling with held-in data on EmbodiedBench. RoboFoundry (GPT-5.5) is evaluated on 50 fixed held-out tasks per suite. Using three quarters of the held-in data already matches the full-data success rate of $74.0\text{\,}\%$ on EB-Habitat, highlighting data-efficient evolution.

Method	Avg.	EB- ALFRED	EB- Habitat	EB- Navigation	EB- Manipulation
RoboFoundry (GPT-6 Astra)	78.0 +6.1	90.0 +2.7	86.7 +11.4	80.2 +3.2	54.9 +6.9
RoboFoundry (GPT-5.5)	72.7 +15.8	84.0 +7.3	88.7 +24.7	72.0 +17.3	45.9 +13.8
RoboFoundry (Qwen3.7-Plus)	70.3 +10.4	81.3 +8.0	80.3 +15.3	71.9 +7.6	47.7 +10.5
RoboFoundry (GLM5.3-Flash)	69.3 +7.4	80.0 +8.3	75.0 +14.3	72.7 +1.7	49.5 +5.1
RoboFoundry (Qwen3.8-27B)	66.5 +14.2	78.0 +12.3	77.0 +19.0	68.3 +16.0	42.8 +9.5
RoboFoundry-Lite (GPT-5.5)	65.5 +8.6	78.0 +1.3	74.3 +10.3	69.7 +15.0	39.9 +7.8

Table 1: Overall comparison on EmbodiedBench. Avg. is the arithmetic mean of success rates across the four suites. All results are reported in percentage (%). Gains are reported in percentage points (+pp).

Method	Overall	Transfer	Occlusion	Counting	Sequence
RoboFoundry	53.5 / 72.8	70.5 / 78.1	38.3 / 57.6	55.2 / 82.5	75.2 / 92.1
PrediMem	38.5 / 55.2	22.5 / 45.2	27.3 / 38.4	45.7 / 69.3	72.5 / 89.5
MemER	27.3 / 49.1	20.0 / 36.1	16.4 / 33.2	27.1 / 65.1	65.0 / 79.1
HiF-VLA	16.9 / 39.8	17.5 / 38.9	12.7 / 27.1	8.6 / 45.9	42.5 / 70.2
$\pi_{0.5}$	21.5 / 38.7	20.0 / 42.8	12.7 / 17.2	14.3 / 50.9	60.0 / 71.6
MemoryVLA	15.0 / 35.3	15.0 / 37.2	7.3 / 13.1	14.3 / 55.1	37.5 / 65.2

Table 2: Long-horizon memory results on RoboMemArena. RoboFoundry achieves the best TSR and CSR overall and across all four categories. Each entry reports TSR / CSR (%).

Table 7

Figure 5: Real-world evolution and transfer of RoboFoundry. (a) Failures motivate alignment and progress-tracking edits, enabling nesting-doll completion in Trial 3. (b) Towel-folding skills evolved on the blue towel transfer to unseen green and pink fabrics. (c) The general system grounds a semantic navigation goal in executable actions on a Unitree G1 humanoid.

Figure 6: Long-horizon chemistry manipulation with RoboFoundry. A task-state record connects six dependent subtasks: funnel readiness and scale taring are written during execution, and the final pouring step reads the earlier funnel-ready state to guide skill execution.

Appendix figures & tables10 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 7: Representative EmbodiedBench cases (GPT-5.5) before and after RoboFoundry evolution. Examples illustrate repairs to perception, reasoning, planning, and low-level skill execution.

Figure 8: RoboFoundry rollouts across four memory categories in RoboMemArena: Transfer, Occlusion, Counting, and Sequence.

Method	Avg.	Base	Common Sense	Complex Instruction	Visual	Spatial	Long Horizon
RoboFoundry (GPT-6 Astra)	90.0	92.0	92.0	92.0	86.0	94.0	84.0
RoboFoundry (GPT-5.5)	84.0	90.0	84.0	84.0	74.0	80.0	92.0
RoboFoundry (Qwen3.7-Plus)	81.3	88.0	90.0	72.0	74.0	82.0	82.0
RoboFoundry (GLM5.3-Flash)	80.0	82.0	76.0	82.0	80.0	74.0	86.0
RoboFoundry (Qwen3.8-27B)	78.0	82.0	84.0	82.0	72.0	68.0	80.0
RoboFoundry-Lite (GPT-5.5)	78.0	82.0	80.0	76.0	70.0	72.0	88.0

Appendix

Table 5: Main comparison on the EB-ALFRED high-level benchmark. Avg. denotes the arithmetic mean over Base, Common Sense, Complex Instruction, Visual, Spatial, and Long Horizon. All results are reported in percentage (%).

Method	Avg.	Base	Common Sense	Complex Instruction	Visual	Spatial	Long Horizon
RoboFoundry (GPT-5.5)	88.7	100.0	78.0	80.0	86.0	100.0	88.0
RoboFoundry (GPT-6 Astra)	86.7	100.0	100.0	100.0	100.0	42.0	78.0
RoboFoundry (Qwen3.7-Plus)	80.3	100.0	80.0	92.0	88.0	48.0	74.0
RoboFoundry (Qwen3.8-27B)	77.0	100.0	58.0	80.0	80.0	94.0	50.0
RoboFoundry (GLM5.3-Flash)	75.0	100.0	58.0	66.0	80.0	94.0	52.0
RoboFoundry-Lite (GPT-5.5)	74.3	100.0	66.0	74.0	80.0	44.0	82.0

Appendix

Table 6: Main comparison on the EB-Habitat high-level benchmark. Avg. denotes the arithmetic mean over Base, Common Sense, Complex Instruction, Visual, Spatial Relationship, and Long Horizon. All results are reported in percentage (%).

Method	Avg.	Base	Common Sense	Complex Instruction	Visual	Long Horizon
RoboFoundry (GPT-6 Astra)	80.3	90.0	85.0	86.7	73.3	66.7
RoboFoundry (GLM5.3-Flash)	72.7	80.0	71.7	78.3	73.3	60.0
RoboFoundry (GPT-5.5)	72.0	80.0	83.3	80.0	76.7	40.0
RoboFoundry (Qwen3.7-Plus)	72.0	78.3	83.3	65.0	66.7	66.7
RoboFoundry-Lite (GPT-5.5)	69.7	83.3	81.7	76.7	80.0	26.7
RoboFoundry (Qwen3.8-27B)	68.3	76.7	85.0	73.3	63.3	43.3

Appendix

Table 7: Main comparison on the EB-Navigation benchmark. Avg. denotes the arithmetic mean over Base, Common Sense, Complex Instruction, Visual, and Long Horizon. All results are reported in percentage (%).

Method	Avg.	Base	Common Sense	Complex Instruction	Visual	Spatial
RoboFoundry (GPT-6 Astra)	54.8	58.3	62.5	56.3	55.6	41.7
RoboFoundry (GLM5.3-Flash)	49.6	56.3	45.8	56.3	47.2	41.7
RoboFoundry (Qwen3.7-Plus)	47.8	43.8	54.2	52.1	44.4	43.8
RoboFoundry (GPT-5.5)	46.1	54.2	47.9	43.8	41.7	41.7
RoboFoundry (Qwen3.8-27B)	42.5	35.4	41.7	43.8	47.2	45.8
RoboFoundry-Lite (GPT-5.5)	39.5	43.8	37.5	31.3	47.2	39.6

Appendix

Table 8: Main comparison on the EB-Manipulation benchmark. Avg. denotes the success rate over all 228 tasks. All results are reported in percentage (%).

Method	Avg.	EB- ALFRED	EB- Habitat	EB- Navigation	EB- Manipulation
RoboFoundry (GPT-5.5)	71.7 +8.3	82.3 +3.3	81.3 +12.3	75.7 +8.4	47.3 +9.0
RoboFoundry (Qwen3.7-Plus)	70.4 +5.8	82.5 +2.8	79.3 +5.6	71.8 +1.0	47.8 +13.7
RoboFoundry (Qwen3.8 27B)	69.4 +5.1	79.7 +3.4	79.3 +8.0	75.0 +6.0	43.7 +3.0
RoboFoundry (GLM5.3-Flash)	68.8 +7.6	80.0 +5.7	72.0 +5.3	77.7 +8.7	45.5 +10.5
Qwen3.7-Plus	64.6	79.7	73.7	70.8	34.1
Qwen3.8-27B	64.3	76.3	71.3	69.0	40.7

Appendix

Table 9: Overall comparison on EmbodiedBench (16K). Avg. is the arithmetic mean of success rates across the four suites. All results are reported in percentage (%). Gains are reported in percentage points (+pp).

Task (Symbolic Form)	OpenVLA		$\bm{\pi_{0}}$		$\bm{\pi_{0.5}}$		CaP-Agent0		RoboFoundry		RoboFoundry †
	Pos	Task	Pos	Task	Pos	Task	Pos	Task	Pos	Task	Pos	Task
$\operatorname{Place}(\texttt{alphabet\_soup},\texttt{basket})$	0.0	0.0	0.0	0.0	0.0	0.0	2.0	4.0	98.0	99.0	100.0	100.0
$\operatorname{Place}(\texttt{bbq\_sauce},\texttt{basket})$	0.0	0.0	0.0	0.0	100.0	2.0	12.0	42.0	97.0	93.0	100.0	100.0
$\operatorname{Place}(\texttt{butter},\texttt{basket})$	0.0	0.0	0.0	0.0	54.0	0.0	26.0	18.0	97.0	99.0	100.0	100.0
$\operatorname{Place}(\texttt{chocolate\_pudding},\texttt{basket})$	0.0	0.0	0.0	0.0	0.0	2.0	18.0	48.0	88.0	93.0	100.0	100.0
$\operatorname{Place}(\texttt{cream\_cheese},\texttt{basket})$	0.0	0.0	10.0	0.0	0.0	0.0	12.0	6.0	98.0	99.0	100.0	100.0

Appendix

Table 10: Task-wise LIBERO-PRO performance on the libero-object benchmark. Each entry reports position/task success rate in percentage (%).

\dagger

denotes the use of privileged simulator object poses. Results for OpenVLA,

\pi_{0}

\pi_{0.5}

, and CaP-Agent0 are taken directly from the CaP-Agent0 report [ Fu et al., 2026 ] .

Task (Symbolic Form)	OpenVLA		$\bm{\pi_{0}}$		$\bm{\pi_{0.5}}$		CaP-Agent0		RoboFoundry		RoboFoundry †
	Pos	Task	Pos	Task	Pos	Task	Pos	Task	Pos	Task	Pos	Task
$\operatorname{Open}(\texttt{cabinet},\texttt{drawer\_mid})$	0.0	0.0	0.0	0.0	0.0	4.0	0.0	0.0	71.0	71.0	95.0	100.0
$\operatorname{Put}(\texttt{bowl},\texttt{drawer\_top})$	0.0	0.0	0.0	0.0	94.0	2.0	4.0	0.0	90.0	71.0	95.0	100.0
$\operatorname{Push}(\texttt{plate},\texttt{stove\_front})$	0.0	0.0	0.0	0.0	0.0	0.0	0.0	10.0	71.0	70.0	95.0	100.0
$\operatorname{Put}(\texttt{bowl},\texttt{plate})$	0.0	0.0	0.0	0.0	0.0	2.0	36.0	38.0	90.0	97.0	100.0	100.0
$\operatorname{Put}(\texttt{bowl},\texttt{stove})$	0.0	0.0	0.0	0.0	0.0	4.0	22.0	12.0	99.0	100.0	95.0	100.0

Appendix

Table 11: Task-wise LIBERO-PRO performance on the libero-goal benchmark. Each entry reports position/task success rate in percentage (%).

\dagger

denotes the use of privileged simulator object poses. Results for OpenVLA,

\pi_{0}

\pi_{0.5}

, and CaP-Agent0 are taken directly from the CaP-Agent0 report [ Fu et al., 2026 ] .

Task (Symbolic Form)	OpenVLA		$\bm{\pi_{0}}$		$\bm{\pi_{0.5}}$		CaP-Agent0		RoboFoundry		RoboFoundry †
	Pos	Task	Pos	Task	Pos	Task	Pos	Task	Pos	Task	Pos	Task
$\operatorname{Pick}(\operatorname{between}(\texttt{plate},\texttt{ramekin}),\texttt{plate})$	0.0	0.0	0.0	0.0	2.0	0.0	22.0	14.0	98.0	88.0	100.0	100.0
$\operatorname{Pick}(\texttt{table\_center},\texttt{plate})$	0.0	0.0	0.0	0.0	0.0	2.0	22.0	14.0	89.0	99.0	100.0	100.0
$\operatorname{Pick}(\operatorname{drawer\_top}(\texttt{cabinet\_wood}),\texttt{plate})$	0.0	0.0	0.0	0.0	0.0	0.0	2.0	10.0	88.0	96.0	99.0	98.0
$\operatorname{Pick}(\operatorname{next\_to}(\texttt{cookie\_box}),\texttt{plate})$	0.0	0.0	0.0	0.0	0.0	2.0	0.0	10.0	97.0	96.0	100.0	98.0
$\operatorname{Pick}(\operatorname{next\_to}(\texttt{plate}),\texttt{plate})$	0.0	0.0	0.0	0.0	0.0	0.0	10.0	20.0	97.0	88.0	100.0	98.0

Appendix

Table 12: Task-wise LIBERO-PRO performance on the libero-spatial benchmark. Each entry reports position/task success rate in percentage (%).

\dagger

denotes the use of privileged simulator object poses. Results for OpenVLA,

\pi_{0}

\pi_{0.5}

, and CaP-Agent0 are taken directly from the CaP-Agent0 report [ Fu et al., 2026 ] .

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