A Backbone for Long-Horizon Robot Task Understanding
Embedded Files
Paper link: https://ieeexplore.ieee.org/abstract/document/10829642
Xiaoshuai Chen[1], Wei Chen[1], Dongmyoung Lee[1], Yukun Ge[1], Nicolas Rojas[2] and Petar Kormushev[1]
1. Imperial College London, England, UK 2. The AI Institute, Cambridge, MA, USA
Foamblock-Flipping
Foamblock-Flipping
Board-Rolling
Board-Rolling
Paper-Stamping
Paper-Stamping
Spoon-Tilting
Spoon-Tilting
Plate-Scrubbing
Plate-Scrubbing
Abstract
Abstract
End-to-end robot learning, particularly for long-horizon tasks, often results in unpredictable outcomes and poor generalization. To address these challenges, we propose a novel Therblig-Based Backbone Framework (TBBF) as a fundamental structure to enhance interpretability, data efficiency, and generalization in robotic systems. TBBF utilizes expert demonstrations to enable therblig-level task decomposition, facilitate efficient action-object mapping, and generate adaptive trajectories for new scenarios.
The approach consists of two stages: offline training and online testing. During the offline training stage, we developed the Meta-RGate SynerFusion (MGSF) network for accurate therblig segmentation across various tasks. In the online testing stage, after a one-shot demonstration of a new task is collected, our MGSF network extracts high-level knowledge, which is then encoded into the image using Action Registration (ActionREG) Additionally, Large Language Model (LLM)-Alignment Policy for Visual Correction (LAP-VC) is employed to ensure precise action execution, facilitating trajectory transfer in novel robot scenarios. Experimental results validate these methods, achieving 94.37% recall in therblig segmentation and success rates of 94.4% and 80% in real-world online robot testing for simple and complex scenarios, respectively. These advances collectively improve the interpretability and applicability of robotic learning systems, addressing critical issues in long-horizon tasks.
video.movVideo Presentation
Video Presentation
Research Motivation
Research Motivation
Current bottleneck
Current bottleneck
Imagine a world where robots seamlessly integrate into our daily lives, performing complex tasks with the same ease and understanding as humans. Despite significant advancements in robotics, current systems struggle with intricate, long-horizon tasks, lacking the adaptability needed for real-world scenarios. Our journey began with the desire to overcome these limitations and unlock the true potential of robotics.
Key ideas
Key ideas
Robots excel at simple tasks like pick-and-place but struggle with complex operations that require multiple steps and environmental understanding, such as pouring liquids or assembling components. Inspired by therbligs—basic motions in time and motion studies—we developed a framework that decomposes complex tasks into fundamental units. This structured method enables robots to understand and execute tasks efficiently.
Our solution has significant commercial potential by simplifying task learning, allowing robots to adapt quickly and perform reliably in various environments after one demonstration. This is advantageous in industries like manufacturing, logistics, and healthcare, where precision, efficiency, and scalability are crucial. Our approach enhances productivity, reduces training time, and lowers operational costs, making robotic integration more practical and beneficial for businesses.
Conceptual Solution
Conceptual Solution
We introduced a novel approach to enhance robot task understanding and generalization for complex, long-horizon tasks. By decomposing high-level tasks into fundamental action units called therbligs, we establish a clear backbone that captures the essential structure of long-horizon tasks. We register the actions from demonstrations directly into images, enabling the robot to infer the relationships between actions and objects within the environmental context.
This integration of action registration and contextual reasoning allows the robot to generalize efficiently from one-shot demonstrations, improving adaptability to new tasks and environments. By focusing on these foundational elements and their visual representations, our method bridges the gap between low-level motions and high-level task comprehension, advancing towards zero-shot generalization capabilities for objects.
Methods-(TBBF Overview)
Methods-(TBBF Overview)
The Therblig-Based Backbone Framework (TBBF) enhances robotic task understanding and execution by decomposing complex tasks into fundamental units called therbligs. This structured approach improves data efficiency and task generalization. The TBBF integrates the Meta-RGate SynerFusion (MGSF) network for accurate therblig segmentation during offline training and uses Action Registration (ActionREG) to ensure precise action execution. This comprehensive framework significantly boosts the interpretability, stability, and adaptability of robotic systems in diverse and dynamic environments.
Methods-(MGSF Network)
Methods-(MGSF Network)
The Meta-RGate SynerFusion (MGSF) network is a state-of-the-art model designed to enhance long-horizon robotic task understanding by accurately segmenting tasks into fundamental units called therbligs. Therbligs, derived from the study of human motions, break down complex robotic tasks into elemental actions, providing a explainable and systematic approach to decompose, analyze and execute complex tasks.
Methods-(ActionReg System)
Methods-(ActionReg System)
ActionREG is an innovative component of our therblig-based backbone framework designed to enhance robotic task execution. By leveraging prior knowledge of fundamental actions (therbligs), ActionREG seamlessly integrates these with the configurations of objects within a robot's visual field. This sophisticated system ensures precise action registration and execution, even in dynamic and complex environments.
Methods-(LAP-VC)
Methods-(LAP-VC)
The LLM-Alignment Policy for Visual Correction (LAP-VC) addresses errors in robot task execution due to suboptimal demonstrations and calibration inaccuracies. Utilizing a Large Language Model (LLM), LAP-VC corrects grasping points in real-time by processing predicted points and scenario images, thereby enhancing accuracy.
This method reduces dependency on precise expert demonstrations and compensates for system errors, improving overall robustness and reliability. LAP-VC ensures precise action execution and effective trajectory transfer in complex scenarios, making it essential for advanced robotic systems.
Experiments-(Online Testing One-shot)
Experiments-(Online Testing One-shot)
Foam Block Flipping
Foam Block Flipping
We utilized our TBBF to enable robots to quickly adapt to new tasks with one-shot demonstration. Before the demonstration, the system has never encountered the task, the scene, or pre-trained objects. After a human expert performs a single demonstration of a new task, the system shuffles configurations online and learns the therbligs.
The MGSF network then extracts high-level knowledge and segments the task into fundamental therbligs. This segmented information is encoded into visual data using ActionREG, integrating the therbligs with object configurations in the robot's visual field. This approach ensures precise action registration and robust task execution, allowing the robot to generate and execute new trajectories efficiently in novel and complex scenarios.
Paper-Stamping
Paper-Stamping
Stamping.movFlipping.movFoam Block Flipping
Foam Block Flipping
Board-Rolling
Board-Rolling
Rolling.mov
Plate Scrubbing
Plate Scrubbing
Experiments-(Baseline)
State Machine
State Machine
In our evaluation, we found that State Machine (SM) approaches can achieve partial success in simple scenarios by leveraging pre-built policies and excellent vision features. However, their effectiveness diminishes significantly in complex scenarios due to the need for highly specific and intricate policies for each task. While SM systems can perform some tasks reliably in controlled, simple environments, their lack of adaptability and difficulty in managing diverse, real-world tasks limit their overall utility. This underscores the advantages of our Therblig-Based Backbone Framework (TBBF), which offers greater flexibility and robustness across various task complexities.
Failure Case (SM)
Failure Case (SM)
Succesful Case (SM)
Succesful Case (SM)
Behaviour Clone
Behaviour Clone
In our study, we verified that Behavior Cloning (BC) fails to learn and execute tasks in complex scenarios but can complete certain tasks in simpler scenarios. Specifically, in simple scenarios, BC demonstrated the ability to perform some tasks successfully, although its overall performance remained unstable and less reliable compared to our proposed framework. This highlights the limitations of BC in handling the complexities and variabilities of real-world robotic tasks, reinforcing the need for more advanced and robust approaches like our Therblig-Based Backbone Framework (TBBF).
Failure Case (BC)
Failure Case (BC)
Success Case (BC)
Success Case (BC)
Experiments-(Failure Analysis)
Key Failure Factors
Key Failure Factors
First, inaccurate object position estimation, caused by image computation errors, hand-eye calibration issues, and systematic errors within the robot system.
Second, the manipulator fails to grasp the object securely due to insufficient rich contact grasping depth, incomplete grip, or collisions with the table that prevent proper engagement.
Third, during the use phase, contact between the tool and environmental objects, including the table, alters the object's grasp position, causing relative movement.
Lastly, task-related overlap with other objects hinders correct position and posture recognition.
Results
Our MGSF network achieved the highest recall of 94.37%, surpassing traditional and deep learning models. In addition, our ablation study showed that the integration of gated fusion and meta-learning significantly improved performance. These results highlight the effectiveness of our MGSF network in providing accurate and robust therblig segmentation for diverse robot tasks.
Therblig Segmentation
Therblig Segmentation
The analysis of therblig segmentation recall across various robot tasks revealed that the MGSF network consistently performs well, with surface wiping achieving the highest recall at 97.17% and tissue sweeping achieving the lowest at 93.88%. This variation in performance is attributed to the complexity of the tasks, where tissue sweeping involves more intricate actions such as effectively putting tissue into a dustpan. Additionally, therbligs like Transport Load (TLoad) and Release showed lower recall rates of 85.56% and 83.41%, respectively.
Visual Error Alignment
Visual Error Alignment
The analysis of the heatmap data shows that the LAP-VC system consistently achieves high alignment performance scores across various tasks, outperforming traditional methods like KNN, SIFT, ORB, AKAZE, FAST, and BRISK. Notably, for the Roller task, the LAP-VC system achieved a score of 0.92, which is second only to the manual alignment score of 0.98. Similarly, in the Spoon task, the LAP-VC scored 0.88, close to the perfect manual score of 1.00. In the Stamp, Sponge, and Scraper tasks, the LAP-VC system scored 0.84, 0.94, and 0.90 respectively, showing a consistent high performance. In comparison to other automated methods, LAP-VC demonstrates superior robustness and reliability.
Success Rate (Long-horizon Task)
Success Rate (Long-horizon Task)
The robot task successful rate comparison table demonstrates the superior performance of our proposed system (TBBF) over other baseline methods.
In simple scenarios (SimScenario), our system achieved an average success rate of 94.4% across five tasks (board-rolling, foamblock-flipping, plate-scrubbing, spoon-tilting, and paper-stamping). In contrast, Single-Task State Machine (ST-SM), Single-Task Behavior Clone (ST-BC), and Multi-Task Behavior Clone (MT-BC) methods had significantly lower success rates, with the highest being only 13.7% for ST-SM.
When tested in complex scenarios (ComScenario), which included cluttered environments with unrelated objects, our system maintained a strong performance with an average success rate of 80%. The baseline methods, however, failed to achieve any successful task completion in these complex scenarios.
These results highlight the robustness and adaptability of our system in both simple and challenging environments, outperforming traditional state machine and behavior cloning approaches.
Conclusion
Conclusion
In our recent paper, we introduced a groundbreaking framework called the Therblig-Based Backbone Framework (TBBF) designed to improve the understanding and execution of robotic tasks. This innovative approach breaks down complex tasks into fundamental action units known as therbligs, resulting in a more structured and interpretable representation of robotic actions. Our Meta-RGate SynerFusion (MGSF) network achieves remarkable accuracy in therblig segmentation, while our Action Registration (ActionREG) system ensures precise action execution by integrating therbligs with object configurations.
Our experimental results are promising, with a therblig segmentation recall rate of 94.37% and successful task execution rates of 94.4% in simple scenarios and 80% in complex scenarios. Despite these successes, we recognize areas for future improvement, including handling noisy demonstrations and addressing the effects of shadows and reflections. Our ongoing research aims to enhance the robustness of the TBBF, expand its applicability to more complex environments, and adapt it to various robotics platforms.
Future Work
Future Work
As we continue to advance the capabilities of the TBBF network, several areas for future research and development are identified to enhance its robustness and applicability in complex and dynamic environments.
Expert Demonstration Dataset Enhancement
Develop methods to handle some extremely noisy or unprofessional user demonstrations, ensuring the network's reliability even with suboptimal input data.
Integration of 3D Object Configurations
Extend the current 2D configuration approach to incorporate depth information, enabling better handling of 3D object configurations and spatial relationships.
Improved Lighting and Shadow Handling
Address the challenges posed by object shadows and light reflections to improve the network's accuracy in various lighting conditions.
Expansion to Diverse Robotics Platforms
Adapt the MGSF network for use with other robotic platforms beyond the UR5, such as Panda and Kinova robots, facilitating broader applicability and transferability of robotic knowledge
BibTex
BibTex
@article{Xiaoshuai2024TBBF,
title={A Backbone for Long-Horizon Robot Task Understanding},
author={Xiaoshuai Chen, Wei Chen, Dongmyoung Lee, Yukun Ge, Nicolas Rojas and Petar Kormushev},
journal={arXiv preprint arXiv: 2408.01334},
year={2024}
}
Additional materials
Additional materials
Comparison of Capacities of Different Robot Systems [Criteria]
1. Data Efficiency:
Definition: Refers to a robot system's capability to learn effectively from a limited dataset. Higher data efficiency means achieving significant performance with fewer demonstrations, crucial in environments where data gathering is expensive or challenging.
Assessment Standards:
High: Achieves results from a single image or demonstration.
Moderate: Requires a single video and trajectory data.
Low: Needs extensive video and trajectory data.
2. Task Horizon:
Definition: Relates to the length and complexity of tasks a robot system can efficiently manage. Systems built for long-horizon tasks handle more complex, multi-step actions, while those for short-horizon tasks are optimized for simpler, quicker tasks.
Assessment Standards:
Long: Tasks predominantly exceed ten steps.
Mixed: Contains both long and short-horizon tasks.
Short: Tasks primarily involve fewer than ten steps.
3. Task Interpretability:
Definition: Measures the ease with which human operators can understand and predict the robot’s actions. High interpretability involves clear, transparent decision-making processes, essential for operations in sensitive or human-centric environments.
Assessment Standards:
High Clarity: Each action step and task-related objects are clear during execution.
Medium Clarity: Actions or task-related objects during steps are ambiguous.
Low Clarity: Both actions and task-related objects are unclear during steps.
4. Task Diversity:
Definition: Evaluates the variety of tasks a robot system can perform. A diverse system demonstrates adaptability across different operations, environments, and manipulations, indicating versatility and resilience.
Assessment Standards:
Wide Domain: Handles a rich variety of tasks involving dynamic interactions and contact.
Narrow Domain: Primarily focuses on basic tasks like pick-and-place, pushing, or hitting.
5. Task Generalization:
Definition: The robot system's ability to apply learned behaviors to new, unseen tasks or conditions. High generalization minimizes the need for retraining when introduced to different settings.
6. Pre-trained Scenarios:
Definition: The extent to which a robot system is equipped with pre-trained models for specific applications. A broad set of pre-trained scenarios allows for quick deployment in diverse environments without significant customization.
Assessment Standards:
Required: Needs objects to be pre-trained and included in an 'object bank' for online testing stages.
Non-Essential: Capable of handling unseen objects without pre-training.
7. Scenario Complexity:
Definition: Considers the number of variables and the degree of uncertainty a robot system manages in its operational environment. Systems that adeptly handle complex scenarios can make nuanced decisions under dynamic or unpredictable conditions.
Assessment Standards:
Complex: Deals with cluttered environments and unseen objects, including four or more unrelated task objects.
Moderate: Involves fewer than four task-related objects.
Simple: Limited to task-related objects only.
8. Multi-modal Fusion:
Definition: The robot system's capacity to integrate and process data from various sources or sensor types. Effective multi-modal fusion improves perception and decision-making, crucial for complex, real-world operations.
Page updated
Google Sites
Report abuse