Emergent Neural Automaton Policies: Learning Symbolic Structure from Visuomotor Trajectories
ENAP learns symbolic structures from visuomotor trajectories, outperforming SOTA methods by 27% in low-data regimes.
Key Findings
Methodology
ENAP combines adaptive clustering and an extended L* algorithm to extract Mealy state machines as high-level planners, guiding a residual network for precise continuous control via behavior cloning.
Key Results
- ENAP achieves a 27% performance improvement over SOTA VLA models in low-data regimes while using 39% fewer parameters.
- It provides structured representations of task logic, such as cyclic dependencies and failure recovery.
- Ablation studies show symbolic structures significantly enhance sample efficiency and interpretability.
Significance
This study addresses challenges in scaling robot learning to long-horizon tasks by combining symbolic reasoning and continuous control, improving efficiency and interpretability, and paving the way for hierarchical cognitive architectures.
Technical Contribution
Introduces a label-free neuro-symbolic framework that autonomously discovers interpretable automata from visuomotor data, bridging the gap between sensorimotor control and symbolic reasoning.
Novelty
ENAP uniquely integrates Mealy state machines with residual networks, enabling adaptive learning of symbolic structures from raw data, unlike traditional methods reliant on handcrafted priors.
Limitations
- Symbol abstraction may be unstable in noisy datasets, affecting state machine quality.
- The extended L* algorithm is sensitive to data sparsity, requiring diverse samples.
Future Work
Future work could explore real-time updates to state machine structures in dynamic environments and integrate reinforcement learning for adaptive low-level control.
AI Executive Summary
Scaling robot learning to long-horizon tasks remains challenging due to the lack of structural priors in end-to-end methods and the rigidity of handcrafted symbolic approaches.
ENAP introduces a bi-level framework that learns symbolic structures from visuomotor trajectories. It combines Mealy state machines for high-level planning and residual networks for precise low-level control. Experiments show ENAP outperforms SOTA methods by 27% in low-data regimes and provides interpretable task logic representations.
This work advances robot cognition by improving efficiency and interpretability. Future directions include dynamic structure updates and enhanced adaptive control strategies.
Deep Analysis
Background
Robot learning has seen rapid advancements, with end-to-end methods excelling in short-horizon tasks. However, scaling to long-horizon tasks requires structured reasoning, which traditional neuro-symbolic methods achieve but rely on handcrafted priors.
Core Problem
Long-horizon tasks demand a synergy between high-level planning and low-level control. Existing methods either lack structural reasoning or depend on predefined symbolic priors, limiting adaptability and efficiency.
Innovation
ENAP introduces a bi-level architecture: high-level Mealy state machines extracted via an extended L* algorithm and low-level residual networks trained via behavior cloning. Unlike prior methods, ENAP autonomously learns symbolic structures from raw data.
Methodology
- �� Adaptive clustering: HDBSCAN discretizes continuous observation space into symbols.
- �� Extended L* algorithm: Constructs Mealy state machines from trajectory data.
- �� Residual network: Combines state machine outputs with learned compensatory terms for precise control.
- �� Ablation studies: Validate the impact of symbolic structures on efficiency and interpretability.
Experiments
Experiments use complex manipulation datasets, comparing ENAP with SOTA VLA models, evaluating sample efficiency in low-data regimes, and conducting ablation studies to analyze symbolic structure contributions.
Results
ENAP achieves 27% performance improvement in low-data regimes, uses 39% fewer parameters, and provides structured task logic representations. Ablation studies highlight the critical role of symbolic structures in enhancing efficiency.
Applications
ENAP is suitable for industrial robotic tasks like assembly and failure recovery, leveraging structured logic for efficient and reliable task execution.
Limitations & Outlook
ENAP is sensitive to data quality, with noisy or sparse datasets potentially affecting stability. The extended L* algorithm requires further optimization for dynamic environments.
Plain Language Accessible to non-experts
Imagine a robot in a kitchen. ENAP acts like a recipe assistant, first observing how to cook (visuomotor trajectories) and summarizing the steps into a recipe (state machine). This recipe guides the robot on what to do at each stage, like 'chop vegetables' or 'boil soup.' Meanwhile, the robot learns to adjust its actions, such as how much force to use when chopping. This combination lets the robot understand the task logic and execute it precisely.
ELI14 Explained like you're 14
Imagine playing a long-level video game. ENAP is like a super cheat sheet that watches how you play and figures out the best strategies, like 'defeat enemies first, then open the door.' It also helps you optimize your moves, like telling you which weapon works best. With ENAP, you can beat the game faster and learn the overall strategy too!
Glossary
Mealy State Machine
A finite state machine where outputs depend on both current state and input.
Used as the high-level planner for task logic.
Extended L* Algorithm
An algorithm for learning state machines from data, extended to support probabilistic and continuous inputs.
Extracts task structures from visuomotor trajectories.
Behavior Cloning
A method of learning control policies by mimicking expert demonstrations.
Trains the residual network for precise control.
HDBSCAN
An adaptive clustering algorithm for discovering discrete symbols from continuous data.
Used in the symbol abstraction phase.
Visuomotor Trajectories
Sequences of visual and motor data during task execution.
Source data for learning structured task logic.
Open Questions Unanswered questions from this research
- 1 How can state machine structures be updated in dynamic environments?
- 2 How can low-level control strategies be made more adaptive?
Applications
Immediate Applications
Industrial Robot Assembly
Uses ENAP's structured logic to improve efficiency and reliability in assembly tasks.
Failure Recovery
Leverages state machine logic for automated fault detection and recovery.
Long-term Vision
General Robot Cognition
Combines symbolic structures and continuous control for interpretable general-purpose intelligent robots.
Abstract
Scaling robot learning to long-horizon tasks remains a formidable challenge. While end-to-end policies often lack the structural priors needed for effective long-term reasoning, traditional neuro-symbolic methods rely heavily on hand-crafted symbolic priors. To address the issue, we introduce ENAP (Emergent Neural Automaton Policy), a framework that allows a bi-level neuro-symbolic policy adaptively emerge from visuomotor demonstrations. Specifically, we first employ adaptive clustering and an extension of the L* algorithm to infer a Mealy state machine from visuomotor data, which serves as an interpretable high-level planner capturing latent task modes. Then, this discrete structure guides a low-level reactive residual network to learn precise continuous control via behavior cloning (BC). By explicitly modeling the task structure with discrete transitions and continuous residuals, ENAP achieves high sample efficiency and interpretability without requiring task-specific labels. Extensive experiments on complex manipulation and long-horizon tasks demonstrate that ENAP outperforms state-of-the-art (SoTA) end-to-end VLA policies by up to 27% in low-data regimes, while offering a structured representation of robotic intent (Fig. 1).