Physics-informed Neural Motion Planning on Constraint Manifolds (2403.05765v1)

Abstract: Constrained Motion Planning (CMP) aims to find a collision-free path between the given start and goal configurations on the kinematic constraint manifolds. These problems appear in various scenarios ranging from object manipulation to legged-robot locomotion. However, the zero-volume nature of manifolds makes the CMP problem challenging, and the state-of-the-art methods still take several seconds to find a path and require a computationally expansive path dataset for imitation learning. Recently, physics-informed motion planning methods have emerged that directly solve the Eikonal equation through neural networks for motion planning and do not require expert demonstrations for learning. Inspired by these approaches, we propose the first physics-informed CMP framework that solves the Eikonal equation on the constraint manifolds and trains neural function for CMP without expert data. Our results show that the proposed approach efficiently solves various CMP problems in both simulation and real-world, including object manipulation under orientation constraints and door opening with a high-dimensional 6-DOF robot manipulator. In these complex settings, our method exhibits high success rates and finds paths in sub-seconds, which is many times faster than the state-of-the-art CMP methods.

• The paper introduces C-NTFields, the first PINN-based CMP solution that integrates the Eikonal equation for efficient motion planning on constraint manifolds.
• It leverages manifold configuration sampling and a novel speed model to ensure collision-free, kinematically compliant trajectories across complex robotic tasks.
• Empirical results show significant reductions in planning time and higher success rates compared to state-of-the-art methods without needing large expert trajectory datasets.

Analyzing Physics-Informed Neural Motion Planning on Constraint Manifolds

The paper presents a pioneering framework in the domain of Constrained Motion Planning (CMP) by introducing a physics-informed neural approach that operates efficiently on constraint manifolds. The CMP problem is imperative in robotics, addressing scenarios where movements are restricted by complex kinematic constraints, such as object manipulation and robot locomotion. Traditional methods have struggled with the intrinsic zero-volume nature of these manifolds, making solutions computationally expensive and time-consuming. This paper's approach uniquely leverages Physics-Informed Neural Networks (PINNs) to overcome these challenges without the need for extensive expert demonstration data.

Problem Context and Existing Solutions

CMP is tasked with finding a collision-free path for a robot that adheres to given kinematic constraints, imposed by manifolds of zero volume in configuration space. Previous solutions often rely on optimization, sampling, or imitation learning approaches. Optimization methods risk local minima while sampling-based methods can be computationally prohibitive due to random sampling requirements. Modern learning-based approaches like CoMPNetX require large datasets of expert trajectories, which are obtained from classical planners, adding a significant computational burden to the process.

Methodological Innovations

The authors propose the first PINN-based CMP solution, termed Constrained-Neural Time Fields (C-NTFields), which integrates the Eikonal equation within constraint manifold planning. This approach stands out by not requiring pre-collected motion paths for training, thus significantly reducing computational overhead in data preparation. Key components of the methodology include:

1. Manifold Configuration Sampling: A strategy that directly samples configurations on the constraint manifolds using Task Space Regions (TSRs). This process ensures samples respect kinematic constraints naturally, avoiding complex projection operations.
2. Speed Definition on Manifolds: A novel speed model is defined, which assigns high speeds to collision-free manifold configurations while reducing speed for off-manifold or obstacle-bound configurations. This model incorporates smooth decays of speed using an exponential function, more suited for manifold constraints compared to the clip function in previous methods.
3. Eikonal Equation with Viscosity: The progressive learning of an extended Eikonal framework incorporating a unique viscosity term to ensure a well-posed problem formulation. This is crucial in environments where the low-dimensional manifolds introduce potential ambiguity in the path planning solutions.
4. Neural Network Architecture: The architecture utilizes high-frequency Fourier embeddings and ResNet-style encodings to represent complex environment configurations and propagates symmetric properties of motion using an innovative symmetry operator.
5. Training and Planning: The training process employs a progressive speed scheduling technique to mitigate convergence challenges, using an isotropic loss function to align predicted and true speeds. The inference stage uses bidirectional updates to progressively converge the start and goal configurations along the manifold in minimal time.

Results and Implications

Empirical evaluations across complex environments, including high-dimensional 6-DOF robotic manipulations and real-world tasks like door opening and object handling, underscore the efficacy of the proposed method. Results indicate significantly lower path planning times compared to state-of-the-art methods, with improvements in maintaining close adherence to constraint manifolds. The method also shows robustness against the intricacies introduced by thin manifolds, maintaining high success rates.

Future Prospects

The proposed approach opens avenues for further exploration into multi-modal constraints commonly present in more dynamic robotics tasks such as legged locomotion. Extending the current framework to account for these constraints may enhance its applicability to broader classes of CMP problems. Additionally, the elimination of extensive expert datasets in training offers a promising outlook for democratizing access to advanced robotic planning systems, fostering innovation across both academic and industry applications.

Conclusion

This paper makes a significant contribution to the field of CMP by integrating physics-informed principles with neural computation, offering a highly efficient and data-agnostic framework for motion planning on constrained manifolds. Through robust theoretical formulations and practical demonstrations, the research sets a new benchmark in the field of time-efficient and constraint-compliant motion planning methodologies.