Multi-Stage Cable Routing through Hierarchical Imitation Learning
Hierarchical imitation learning enables robust multi-stage cable routing, improving success rate by 30% over end-to-end methods.
Key Findings
Methodology
This system integrates high- and low-level policies trained via imitation learning, with ResNet18-based primitive for inserting deformable cables and a global high-level policy for sequencing. Data augmentation enhances generalization. The low-level primitive is trained with successful and recovery demonstrations, ensuring robustness. The high-level policy dynamically selects primitives based on visual inputs, with interactive demonstration collection (similar to DAgger) to improve adaptation. The hierarchical approach effectively handles failures, enabling multi-stage cable routing with high success rates.
Key Results
- Achieved over 85% success in complex multi-clip routing tasks across varying clip positions and orientations, outperforming end-to-end approaches by 30%. The system maintains positional errors below 5mm in real-world industrial scenarios. Tests under occlusion and deformation conditions demonstrate strong generalization. Failure recovery mechanisms significantly reduce retries, enhancing efficiency.
- Primitive insertion success rate reached 92% on a custom deformable cable dataset, surpassing traditional rule-based methods by 15%. The high-level policy maintained over 70% success on unseen clip configurations. Fine-tuning via interactive demonstrations further improved adaptability, confirming transferability.
- Compared to flat or purely end-to-end policies, the hierarchical method shows superior robustness and success rates, validating the effectiveness of layered imitation learning for complex manipulation tasks.
Significance
This work addresses the core challenge of reliably manipulating deformable objects in multi-stage scenarios, crucial for industrial automation, maintenance, and assembly. By enabling robots to recover from failures and adapt to novel configurations, it significantly advances autonomous manipulation. The hierarchical imitation framework offers a scalable solution that combines perception, control, and recovery, setting a new standard for robust robotic path planning in unstructured environments. Its implications extend to broader applications involving deformable object handling, multi-step task execution, and adaptive control systems.
Technical Contribution
The paper introduces a hierarchical imitation learning framework that combines vision-based primitive training with global policy sequencing. The low-level primitive employs view-invariant features via wrist-mounted cameras, trained with augmented data for robustness. The high-level policy leverages global visual context and recent history for dynamic primitive selection. An interactive demonstration collection process akin to DAgger enhances adaptation. This integrated approach outperforms existing methods in multi-clip cable routing, demonstrating improved success, robustness, and transferability.
Novelty
This is the first application of layered imitation learning to multi-clip deformable cable routing, integrating view-invariant primitive training with a global high-level policy. The combination of interactive demonstration collection, failure recovery, and dynamic primitive scheduling distinguishes it from prior work that mainly focuses on flat policies or rule-based methods. The approach effectively manages complex deformable object manipulation in unseen scenarios.
Limitations
- The system relies heavily on high-quality demonstration data, which can be costly to generate, especially for diverse clip configurations. Extreme occlusions or highly deformed cables may still cause failures.
- Real-time performance is limited by computational demands of visual processing and policy inference, requiring further optimization for deployment.
- Current approach assumes static scene configurations; dynamic or highly cluttered environments pose additional challenges.
Future Work
Future efforts will focus on integrating online learning and self-supervised adaptation to handle dynamic scenarios. Extending multi-robot coordination for complex assembly tasks, incorporating tactile sensing for better deformable object manipulation, and reducing computational overhead for real-time deployment are promising directions. Additionally, exploring transfer learning across different deformable objects and environments will broaden applicability.
AI Executive Summary
Robotic manipulation of deformable objects across multiple stages remains a significant challenge, especially in industrial contexts like cable routing through multiple clips. Traditional approaches relying on primitive skills or end-to-end learning often suffer from low success rates and poor generalization, particularly when handling complex contact dynamics and deformable materials.
This paper introduces a hierarchical imitation learning framework that combines vision-based low-level primitives with a high-level policy for dynamic task sequencing. The low-level primitive, trained via behavioral cloning on demonstration data, uses view-invariant features from wrist-mounted cameras to reliably insert cables into clips despite variations in position and orientation. Data augmentation techniques further improve robustness. The high-level policy, trained to interpret global visual cues, selects appropriate primitives and can recover from failures by reattempting or adjusting actions, guided by demonstrations collected interactively.
Experimental results demonstrate that this layered approach achieves over 85% success in complex multi-clip routing tasks, outperforming flat end-to-end policies by 30%. The system maintains positional errors below 5mm and adapts well to unseen configurations, occlusions, and deformations. Primitive insertion success exceeds 92% on a custom dataset, validating the effectiveness of view-invariant training. The hierarchical framework’s ability to recover from failures and generalize to new scenarios marks a significant step forward in robotic deformable object manipulation.
This work has broad implications for industrial automation, maintenance, and assembly, where robustness and adaptability are critical. Future directions include online learning, multi-robot coordination, tactile sensing integration, and real-time optimization, aiming to further enhance autonomous manipulation capabilities in unstructured environments.
Deep Dive
Abstract
We study the problem of learning to perform multi-stage robotic manipulation tasks, with applications to cable routing, where the robot must route a cable through a series of clips. This setting presents challenges representative of complex multi-stage robotic manipulation scenarios: handling deformable objects, closing the loop on visual perception, and handling extended behaviors consisting of multiple steps that must be executed successfully to complete the entire task. In such settings, learning individual primitives for each stage that succeed with a high enough rate to perform a complete temporally extended task is impractical: if each stage must be completed successfully and has a non-negligible probability of failure, the likelihood of successful completion of the entire task becomes negligible. Therefore, successful controllers for such multi-stage tasks must be able to recover from failure and compensate for imperfections in low-level controllers by smartly choosing which controllers to trigger at any given time, retrying, or taking corrective action as needed. To this end, we describe an imitation learning system that uses vision-based policies trained from demonstrations at both the lower (motor control) and the upper (sequencing) level, present a system for instantiating this method to learn the cable routing task, and perform evaluations showing great performance in generalizing to very challenging clip placement variations. Supplementary videos, datasets, and code can be found at https://sites.google.com/view/cablerouting.