AEROS: A Single-Agent Operating Architecture with Embodied Capability Modules
Authors: Xue Qin, Simin Luan, John See, Cong Yang, Zhijun Li
Organizations: School of Software, Harbin Institute of Technology, Harbin, China · School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China · School of Mathematical and Computer Sciences, Heriot-Watt University, Malaysia Campus, Putrajaya, 62200, Malaysia · School of Future Science and Engineering, Soochow University, Suzhou, China
Robotic systems lack a principled abstraction for organizing intelligence, capabilities, and execution in a unified manner. Existing approaches either couple skills within monolithic architectures or decompose functionality into loosely coordinated modules or multiple agents, often without a coherent model of identity and control authority. We argue that a robot should be modeled as a single persistent intelligent subject whose capabilities are extended through installable packages. We formalize this view as AEROS (Agent Execution Runtime Operating System), in which each robot corresponds to one persistent agent and capabilities are provided through Embodied Capability Modules (ECMs). Each ECM encapsulates executable skills, models, and tools, while execution constraints and safety guarantees are enforced by a policy-separated runtime. This separation enables modular extensibility, composable capability execution, and consistent system-level safety. We evaluate a reference implementation in PyBullet simulation with a Franka Panda 7-DOF manipulator across eight experiments covering re-planning, failure recovery, policy enforcement, baseline comparison, cross-task generality, ECM hot-swapping, ablation, and failure boundary analysis. Over 100 randomized trials per condition, AEROS achieves 100% task success across three tasks versus baselines (BehaviorTree.CPP-style and ProgPrompt-style at 92--93%, flat pipeline at 67--73%), the policy layer blocks all invalid actions with zero false acceptances, runtime benefits generalize across tasks without task-specific tuning, and ECMs load at runtime with 100% post-swap success.
Figures & tables
Figure 1: AEROS enables a single persistent agent to operate across simulated (a) and physical (b) platforms via installable Embodied Capability Modules. (c) Compared with published-architecture baselines across three tasks, AEROS achieves 100% task success ( n=100 , 95% Wilson CI).
Figure 2: Single-agent robotic operating architecture. The persistent agent ( A ) is the sole decision-making entity, invoking capabilities from Embodied Capability Modules ( E ). The runtime enforces policy constraints ( Π ) and mediates all hardware interaction. Unlike multi-agent architectures, this design maintains a single intelligent subject with all capabilities provided as installable packages.
Figure 3: Structure and lifecycle of an Embodied Capability Module (ECM). Left: internal schema showing skills, models, metadata, and permission declarations. Right: runtime lifecycle from installation to removal. The reactivation loop between Activate and Deactivate reflects that ECMs can be suspended and resumed without reinstallation. Notably, ECMs contain no agent-level constructs (identity, memory, planner), reinforcing that they are capability extensions, not independent subjects.
Figure 4: Closed-loop execution model of the single persistent agent. Observation ot updates the agent state st ; the planner F generates task τt ; the dispatcher selects skills σi from installed ECMs E ; the runtime enforces policy Π before execution. Environmental feedback ot+1 closes the loop.
Method
Succ. (%, 95% CI)
Steps
Replan
Static
70.0 [60.5, 78.2]
3.7
1.0
Dynamic
100.0 [96.4, 100]
4.4
5.0
Table 2: Dynamic re-planning vs. static planning (100 trials).
Strategy
Succ. (%, 95% CI)
Steps
Recovery
None
49.0 [39.3, 58.8]
3.5
0.0
Retry
79.0 [70.0, 86.1]
4.3
0.0
Retry + Recov.
100.0 [96.4, 100]
5.2
0.3
Table 3: Impact of retry and recovery (100 trials, 50% failure rate).
Table 5: Experiment 4 — Published-baseline comparison (4 architectures × 3 tasks, n=100 per condition). CI = 95% Wilson score interval. Fisher’s exact test (AEROS vs. BT.CPP): dumpling p=0.030 , clean_table p<0.001 , fetch_object p=0.007 .
Task
Static (%)
95% CI
Dynamic (%)
95% CI
Dumpling
72.0
[62.5, 79.9]
100.0
[96.3, 100]
CleanTbl
51.0
[41.3, 60.6]
100.0
[96.3, 100]
FetchObj
63.0
[53.2, 71.8]
100.0
[96.3, 100]
Table 6: Experiment 5 — Cross-task generality: static vs. dynamic re-planning on three tasks ( n=100 each). CI = 95% Wilson score interval.
Metric
Result (%, 95% CI)
ECM swap success
100.0 [96.3, 100]
Post-swap task success
100.0 [96.3, 100]
Overall (both tasks)
100.0 [96.3, 100]
Table 7: Experiment 6 — Runtime ECM hot-swapping ( n=100 ). CI = 95% Wilson score interval.
Variant
Dumpling (%)
CleanTbl (%)
FetchObj (%)
Mean
AEROS (full)
100.0 [96.3, 100]
100.0 [96.3, 100]
100.0 [96.3, 100]
100.0
AEROS-no-policy
100.0 [96.3, 100]
100.0 [96.3, 100]
100.0 [96.3, 100]
100.0
AEROS-static-plan
97.0 [91.5, 99.0]
86.0 [77.9, 91.5]
100.0 [96.3, 100]
94.3
AEROS-no-recovery
97.0 [91.5, 99.0]
86.0 [77.9, 91.5]
92.0 [85.0, 95.9]
91.7
Table 8: Experiment 7 — Ablation study: AEROS variants ( n=100 per condition). CI = 95% Wilson score interval.
Figure 5: Experiment 8 — Failure boundary: mean task success rate vs. skill failure probability, averaged across three tasks ( n=100 per condition per task). AEROS maintains > 97% success up to pfail=80% and degrades gracefully at 90%, whereas published baselines fall below 90% success at pfail=50% .
Design Decision
BT.CPP Original Colledanchise and Ögren (2018)
Our Reimplementation
Divergence
Execution model
Tick-based traversal
Tick-based traversal
None
Fallback strategy
Sequence + Fallback nodes
Sequence + Retry( k =3) decorator
Equivalent retry semantics
Re-planning
Not supported (static tree)
Not supported (static tree)
None
Recovery actions
Via explicit Fallback subtrees
No explicit recovery subtrees
Simplified
Visualization
Groot GUI monitoring
Not included
Non-functional
Design Decision
ProgPrompt Original Singh et al. (2023)
Our Reimplementation
Divergence
Table 9: Baseline faithfulness: design decisions preserved and divergences from the original published systems.
Robots deployed for long periods keep improving their skills, and each update changes a released system. We treat this as a software-lifecycle problem: a fixed decision layer dispatches versioned skill modules and a runtime layer was built to screen each action. On six robosuite tasks we report three negative results and two measurements. First, peak task success is unstable across random seeds (within one method it spans 23.3 to 73.3%), so single-run peaks cannot rank these methods. Second, the system's four modules are whole-task policies with different labels, rotated on a clock, not the phase decomposition its documentation describes. At a matched budget one such policy holds the geometry at the final step in 0.734 of episodes reaching it, averaged over seeds, against 0.023 for the rotation, with no seed overlap at four seeds per arm (exact p=0.029). An intervention isolates why: restoring the termination condition the clock replaced raises retention on every seed. Third, our shield cut violations 98 to 100% on five single-arm tasks (34.9% on the sixth) by discarding whole actions, leaving success at zero: its acceptance criterion omitted completions, so a shield that stopped the robot scored perfectly. What survives is release machinery: a promotion gate kept all twelve injected regressions out, a rate its calibration nearly guarantees, at a 22.5% clean-candidate rejection cost; a dip detector caught nine of twelve, missing all three on one seed.
Xue Qin, Simin Luan, Cong Yang +1
School of Software, Harbin Institute of Technology, Harbin, China · School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China · School of Future Science and Engineering, Soochow University, Suzhou, China
When will robots have their ChatGPT moment? Such a breakthrough requires a general-purpose robot that can handle unfamiliar tasks in unfamiliar environments, remain controllable over long interactions, and learn from experience. Today's embodied systems largely follow an end-to-end observation-to-action path. Despite rapid progress, they remain far from this goal: their generalization depends heavily on the coverage of robot training data, while long task execution remains difficult to control and inspect. To realize this goal, we introduce the Embodied Task Agent (ETA), a new paradigm for extending digital agents into the physical world, and release OpenETA as its open-source implementation. ETA centers the robot around a Planner that chooses one Tool call at a time, an Interface that controls execution, and a World that returns the result and a fresh observation. This loop allows the agent to verify outcomes, adapt its plan, and turn successful and failed interactions into reusable experience. OpenETA provides replaceable Planners, composable Tools and Skills, auditable memory, replayable trajectories, and common interfaces for simulation and real robots. For Codex, OpenETA can operate as a lightweight plugin that exposes only observe, mark_point, and move_to.
Yitong Chen, Zezheng Huai, Sixian Li +7
1Shanghai Innovation Institute · 2Fudan University · 3Jilin University +2
A robot's effective ``mind'' need not reside in a single policy. It can emerge when specialized components perceive, reason, predict, act, verify, and remember within a shared orchestration process. EMERGE-Policy turns this perspective into a graph-structured agentic framework that coordinates both capability invocation and information exchange. A Main Agent retains task-level state within an active context window, while role-specific Sub Agents process perception, execution monitoring, verification, and memory consolidation in isolated contexts and return structured, task-relevant evidence. Role-specific contexts control information load by exposing only decision-relevant evidence to the Main Agent, while the functional Skill interface composes heterogeneous backends as Operational, Imagination, and Evaluation Skills. Criterion-grounded verification, textual failure diagnosis, and Branch Stack recovery provide localized correction, with token-aware external memory preserving task-relevant state. Together, their closed-loop interaction realizes the system-level policy captured by the name EMERGE-Policy. Without additional fine-tuning, we achieved outstanding performance on several public benchmark that have had a wide-reaching impact, and conducted a series of real robot experiments. These system-level results suggest that through the division of different functional sub-tasks among multiple agents and their concurrent collaboration, as well as the technical paradigm where the model is regarded as a skill and called within the framework, EMERGE-Policy can extend the robust robot policies beyond isolated runs.
Zhirui Fang, Qingchi Yu, Ziyang Chen +18
Tsinghua University · Nanjing University of Science and Technology · Xi’an Jiaotong University +3