insight - Robotics - # Articulated Object Manipulation

Precise Articulated Object Manipulation through Online Axis Estimation and SAM2-Based Tracking

Q: How can the proposed axis estimation approach be extended to handle more complex articulated objects with multiple joints or degrees of freedom?

To extend the proposed axis estimation approach for handling more complex articulated objects with multiple joints or degrees of freedom, several strategies can be implemented. First, the axis estimation module can be enhanced to incorporate a hierarchical model of the articulated object, where each joint's kinematic relationship is explicitly defined. This would involve creating a multi-joint model that captures the interactions between different joints, allowing for a more comprehensive understanding of the object's motion dynamics. Second, the integration of advanced machine learning techniques, such as deep reinforcement learning, could be employed to learn the joint configurations and their corresponding motion axes from a diverse dataset of articulated objects. By training on various configurations and manipulation scenarios, the model can generalize better to unseen articulated objects with complex joint arrangements. Additionally, the segmentation process can be refined to better isolate individual moving parts of the articulated object, even in the presence of occlusions. This could involve using a combination of SAM2-based segmentation with temporal tracking algorithms that leverage previous frames to predict the location of occluded parts. By continuously updating the segmentation masks based on the object's motion and interaction dynamics, the system can maintain accurate axis estimation even in complex scenarios. Finally, incorporating feedback mechanisms that utilize real-time sensory data, such as tactile or force feedback, can further enhance the axis estimation process. This feedback can provide additional context about the interaction dynamics, allowing the system to adjust its axis estimation in response to unexpected changes in the object's state or environment.

Q: What are the potential limitations of the SAM2-based segmentation in handling occlusions or partial visibility of the articulated objects during manipulation?

The SAM2-based segmentation, while powerful, has inherent limitations when it comes to handling occlusions or partial visibility of articulated objects during manipulation. One significant limitation is its reliance on visual input, which can be severely affected by occlusions. When parts of an articulated object are obscured from the camera's view, SAM2 may struggle to accurately segment the visible components, leading to incomplete or erroneous segmentation masks. Moreover, SAM2's performance can degrade in scenarios where the articulated object undergoes rapid movements or complex interactions. In such cases, the segmentation may not keep pace with the object's dynamics, resulting in outdated or inaccurate masks that do not reflect the current state of the object. This can hinder the axis estimation process, as the motion axis is derived from the segmented point cloud data. Additionally, SAM2 may face challenges in distinguishing between overlapping objects or parts that are visually similar, particularly in cluttered environments. This can lead to misidentification of the moving components, further complicating the axis estimation and manipulation tasks. To mitigate these limitations, it may be beneficial to integrate SAM2 with other perception modalities, such as depth sensing or multi-view imaging, which can provide additional context and information about the occluded parts. This multi-modal approach can enhance the robustness of the segmentation process, allowing for more accurate and reliable manipulation of articulated objects in real-world scenarios.

Conceitos Básicos

Our closed-loop pipeline integrates interactive perception with online refined axis estimation, enabling adaptive and precise control during manipulation tasks involving articulated objects.

Resumo

The paper presents a novel approach for manipulating articulated objects, which combines interactive perception techniques with online axis estimation derived from SAM2-based tracking of 3D point clouds.

Key highlights:

Closed-loop integration of interactive perception and real-time axis estimation: By continuously refining the motion axis based on updates from 3D point clouds, the approach enables adaptive and precise control during manipulation tasks, overcoming the limitations of open-loop methods.
Utilization of advanced segmentation models for precise articulation recognition: The pipeline employs Grounding DINO for object detection and SAM2 for accurate segmentation, allowing the robot to isolate moving components and calculate the motion axis of articulated objects in real-time.
Enhanced generalization and precision in articulated object manipulation: The method significantly improves manipulation precision and generalization across diverse cabinets in door-opening and drawer-opening tasks, outperforming state-of-the-art baselines and providing consistent axis-aware manipulation.

The experiments demonstrate that the proposed approach outperforms baseline methods, especially in tasks that demand precise axis-based control, such as opening doors to wider angles or drawers to greater extents.

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Estatísticas

The robot needs to open the door larger than 8.6°, 10°, 20°, 30°, 40°, 45°, 50°, 55°, 60°, 65°, 70°.
The robot needs to open the drawer larger than 10 cm, 15 cm, 20 cm, 25 cm, 30 cm, 35 cm, 40 cm, 45 cm.

Citações

"Our closed-loop integration of interactive perception with online refined axis estimation allows for more precise and adaptive control, addressing the limitations of traditional methods that operate in an open-loop fashion."
"By continuously updating the robot's understanding of the object's kinematic state, our approach ensures that the robot maintains effective control throughout the manipulation task."

Principais Insights Extraídos De

Articulated Object Manipulation using Online Axis Estimation with SAM2-Based Tracking

by Xi Wang, Tia... às arxiv.org 09-25-2024

https://arxiv.org/pdf/2409.16287.pdf

Articulated Object Manipulation using Online Axis Estimation with SAM2-Based Tracking

Perguntas Mais Profundas

How can the proposed axis estimation approach be extended to handle more complex articulated objects with multiple joints or degrees of freedom?

To extend the proposed axis estimation approach for handling more complex articulated objects with multiple joints or degrees of freedom, several strategies can be implemented. First, the axis estimation module can be enhanced to incorporate a hierarchical model of the articulated object, where each joint's kinematic relationship is explicitly defined. This would involve creating a multi-joint model that captures the interactions between different joints, allowing for a more comprehensive understanding of the object's motion dynamics.
Second, the integration of advanced machine learning techniques, such as deep reinforcement learning, could be employed to learn the joint configurations and their corresponding motion axes from a diverse dataset of articulated objects. By training on various configurations and manipulation scenarios, the model can generalize better to unseen articulated objects with complex joint arrangements.
Additionally, the segmentation process can be refined to better isolate individual moving parts of the articulated object, even in the presence of occlusions. This could involve using a combination of SAM2-based segmentation with temporal tracking algorithms that leverage previous frames to predict the location of occluded parts. By continuously updating the segmentation masks based on the object's motion and interaction dynamics, the system can maintain accurate axis estimation even in complex scenarios.
Finally, incorporating feedback mechanisms that utilize real-time sensory data, such as tactile or force feedback, can further enhance the axis estimation process. This feedback can provide additional context about the interaction dynamics, allowing the system to adjust its axis estimation in response to unexpected changes in the object's state or environment.

What are the potential limitations of the SAM2-based segmentation in handling occlusions or partial visibility of the articulated objects during manipulation?

The SAM2-based segmentation, while powerful, has inherent limitations when it comes to handling occlusions or partial visibility of articulated objects during manipulation. One significant limitation is its reliance on visual input, which can be severely affected by occlusions. When parts of an articulated object are obscured from the camera's view, SAM2 may struggle to accurately segment the visible components, leading to incomplete or erroneous segmentation masks.
Moreover, SAM2's performance can degrade in scenarios where the articulated object undergoes rapid movements or complex interactions. In such cases, the segmentation may not keep pace with the object's dynamics, resulting in outdated or inaccurate masks that do not reflect the current state of the object. This can hinder the axis estimation process, as the motion axis is derived from the segmented point cloud data.
Additionally, SAM2 may face challenges in distinguishing between overlapping objects or parts that are visually similar, particularly in cluttered environments. This can lead to misidentification of the moving components, further complicating the axis estimation and manipulation tasks.
To mitigate these limitations, it may be beneficial to integrate SAM2 with other perception modalities, such as depth sensing or multi-view imaging, which can provide additional context and information about the occluded parts. This multi-modal approach can enhance the robustness of the segmentation process, allowing for more accurate and reliable manipulation of articulated objects in real-world scenarios.

How could the integration of this axis estimation technique with other perception modalities, such as tactile sensing or force feedback, further enhance the robustness and adaptability of the manipulation pipeline?

Integrating the axis estimation technique with other perception modalities, such as tactile sensing or force feedback, can significantly enhance the robustness and adaptability of the manipulation pipeline. Tactile sensing provides direct information about the physical interaction between the robot and the articulated object, allowing the system to detect subtle changes in contact forces and surface textures. This information can be invaluable for refining the axis estimation process, as it enables the robot to adjust its actions based on real-time feedback from the manipulation task.
For instance, if the robot encounters unexpected resistance while manipulating an articulated object, tactile sensors can detect this change and inform the axis estimation module to reassess the motion axis. This feedback loop can lead to more adaptive control strategies, allowing the robot to modify its manipulation approach dynamically, ensuring successful task completion even in the face of uncertainties.
Similarly, integrating force feedback can provide insights into the forces exerted on the articulated object during manipulation. By measuring the forces applied at different joints, the system can infer the object's response and adjust the axis estimation accordingly. This is particularly useful in scenarios where the articulated object may experience non-linear behavior or when the joints exhibit friction or backlash.
Moreover, combining tactile and force feedback with the existing visual data from SAM2 can create a more comprehensive understanding of the manipulation context. This multi-modal perception approach can enhance the system's ability to handle complex interactions, such as those involving occlusions or dynamic environments, leading to improved precision and efficiency in articulated object manipulation tasks.
In summary, the integration of axis estimation with tactile sensing and force feedback can create a more resilient manipulation pipeline, enabling robots to adapt to real-time changes in their environment and improve their performance in handling articulated objects.