Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning (2110.00792v1)

Abstract: This work proposes an optimization-based manipulation planning framework where the objectives are learned functionals of signed-distance fields that represent objects in the scene. Most manipulation planning approaches rely on analytical models and carefully chosen abstractions/state-spaces to be effective. A central question is how models can be obtained from data that are not primarily accurate in their predictions, but, more importantly, enable efficient reasoning within a planning framework, while at the same time being closely coupled to perception spaces. We show that representing objects as signed-distance fields not only enables to learn and represent a variety of models with higher accuracy compared to point-cloud and occupancy measure representations, but also that SDF-based models are suitable for optimization-based planning. To demonstrate the versatility of our approach, we learn both kinematic and dynamic models to solve tasks that involve hanging mugs on hooks and pushing objects on a table. We can unify these quite different tasks within one framework, since SDFs are the common object representation. Video: https://youtu.be/ga8Wlkss7co

• The paper introduces novel kinematic and dynamic models as functionals of SDFs, capturing holistic object geometry for effective planning.
• The framework integrates learned SDF-based functionals into trajectory optimization, outperforming traditional object representations like point-clouds.
• Experimental results show high task success, with mug hanging achieving 98.7% viability and robust performance in pushing tasks.

Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning

The paper under discussion presents an advanced optimization-based manipulation planning framework wherein learned functionals of signed-distance fields (SDFs) are used to represent objects in a scene. This innovative approach tackles manipulation planning, a field traditionally challenged by the complexity of robot motion tasks in high-dimensional, non-convex spaces. Through the utilization of SDFs, the authors aim to enhance model accuracy and maintain a close connection with perception spaces, which is critical for addressing the limitations of traditional Task and Motion Planning (TAMP) frameworks that depend on analytically defined models and abstractions.

Signed-distance fields are utilized as intermediate representations of objects, bridging raw data sources such as point-clouds and images, with full state information. This framework's underlying premise is that SDF-based models are conducive for optimization-based planning by capturing detailed geometric data and offering informative gradients that are advantageous for planning algorithms. The paper empirically validates this approach by experimenting with two distinctly different manipulation tasks: hanging mugs on hooks and pushing diverse geometric objects to specified goal regions on a surface.

Key Contributions and Results

1. Novel Model Learning: The authors propose methodologically distinct kinematic and dynamic models as functionals of SDFs. The insight here is that SDFs provide a comprehensive representation capturing the geometry of objects, enabling models to consider interactions holistically rather than at discrete contact points alone.
2. Optimization Framework: The manipulation planning framework uses these learned SDF-based functionals as constraints within the trajectory optimization problem. It demonstrates an ability to efficiently solve tasks requiring complex interactions between objects, outperforming other object representations such as point-clouds and occupancy measures.
3. Experimental Validation: Empirical results show that using SDFs with optimization and sampling achieves high rates of success and stability in manipulation tasks, with the mug hanging task achieving up to 98.7% success in finding viable configurations. Furthermore, the pushing task demonstrated nearly complete median coverage of objects within the goal region during open-loop execution.

Implications and Future Developments

The practical implication of this paper is a significant step towards bridging perception and manipulation with learned models that do not rely solely on predictively accurate models but focus on efficient reasoning within a planning context. Theoretically, this work suggests a shift in manipulation planning towards using representations that retain detailed geometrical information for interaction modeling, providing a foundation for future development in AI-driven robotics.

The adaptability of the framework indicates potential scalability to more complex systems, including those involving deformable objects. Additionally, the paper hints at future applications in scenes involving robotic systems, exemplifying how models learned from object-centric interactions can be seamlessly integrated into broader robotic planning problems without additional model retraining.

In conclusion, while local minima remain a challenge, the use of informed sampling strategies and the properties of SDFs collectively indicate promising directions for subsequent research and refinement of planning frameworks. The findings offer a robust alternative to traditional methods, leveraging SDFs' differentiability and comprehensive geometric encoding for advanced manipulation task planning.