Combining Sampling- and Gradient-based Planning for Contact-rich Manipulation

Core Concepts

Proposing a hybrid planning method combining sampling and gradient-based approaches to improve performance in contact-rich manipulation tasks.

Abstract

The content discusses the challenges of planning for contact-rich manipulation due to discontinuous dynamics. It introduces a planning method that combines sampling and gradient-based techniques, leveraging the Cross-entropy Method (CEM) to enhance initialization and handle state constraints. The approach is validated in MuJoCo environments, showing improved performance in contact transitions.
1. Introduction:

Contact-rich manipulation tasks involve dynamic environmental constraints.
Safety and robustness are crucial for handling discontinuous dynamics.
2. Related Works:

Model-based planning with complementary conditions for contact constraints.
Hybrid dynamics models have been used for different contact modes.
3. Online Planning Method:

Estimation of hybrid state using a particle filter.
Proposed Cross-Entropy Method (CEM) for action sampling and trajectory evaluation.
Model Predictive Control (MPC) problem formulation with multiple-shooting transcription.
4. Simulation Studies:

Performance comparison on MuJoCo Gym environments.
Contrast between CEM and MPC in stiff contact scenarios.
5. Experiments:

Validation of the proposed approach on real-world vertical and pivot contact tasks.

Stats

Sampling-based planners have higher sample complexity in high-dimensional problems.
Gradient-based solvers can suffer from local optima and poor convergence rates on non-smooth problems.
The proposed approach leverages CEM to initialize a gradient-based solver.

Quotes

"Sampling-based planners have been used in contact planning."
"The proposed approach improves over both MPC and CEM."

Key Insights Distilled From

Combining Sampling- and Gradient-based Planning for Contact-rich Manipulation

by Filippo Rozz... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2310.04822.pdf

Deeper Inquiries

How can the proposed hybrid planner handle scalability issues as dimensions grow?

The proposed hybrid planner addresses scalability issues by leveraging a combination of sampling and gradient-based methods. As dimensions grow, sampling-based planners typically require more samples for convergence, leading to increased computational complexity. However, by incorporating gradient-based solvers into the planning process, the hybrid planner can provide better initialization to handle high-dimensional problems efficiently. The Cross-Entropy Method (CEM) is used to initialize the gradient-based solver, improving convergence rates and enabling explicit handling of state constraints. This approach allows for a more effective utilization of both sampling and gradients in planning tasks with higher dimensions.

What are the implications of using both sampling and gradient methods on computational efficiency?

Integrating both sampling and gradient methods in planning has significant implications for computational efficiency. Sampling-based planners excel at exploring complex spaces without relying on smooth gradients but may struggle with high-dimensional problems due to sample complexity. On the other hand, gradient-based solvers offer faster convergence but are prone to local optima and challenges in non-smooth environments.
By combining these two approaches, the proposed method capitalizes on their respective strengths while mitigating their weaknesses. The use of CEM for initialization enhances the performance of gradient-based optimization by providing a good starting point derived from sampled trajectories. This not only improves convergence rates but also enables explicit handling of state constraints such as force limits.
Overall, this integration results in a more robust planning framework that balances exploration capabilities from sampling with efficient optimization from gradients, ultimately enhancing computational efficiency in contact-rich manipulation tasks.

How does the proposed method compare to other combined planning approaches?

The proposed method stands out among other combined planning approaches by offering a unique blend of sampling- and gradient-based techniques tailored specifically for contact-rich manipulation tasks with discontinuous dynamics.

Initialization Advantage: By utilizing CEM to initialize a gradient-based solver, it provides an edge over traditional combined planners that may lack efficient initialization strategies.
Explicit Handling of State Constraints: Unlike some existing methods that struggle with enforcing explicit safety constraints in non-smooth environments, this approach excels at addressing state constraints like force limits effectively.
Improved Performance: Through empirical validation on MuJoCo Gym environments and real-world contact tasks, the proposed method demonstrates enhanced performance compared to standalone MPC or CEM solutions.
Scalability Enhancement: The ability to improve solve time even as problem stiffness increases or planning horizons lengthen showcases its scalability benefits over certain existing combined planners.

In essence, this novel approach offers a comprehensive solution that leverages complementary aspects of both types of planners while overcoming key limitations seen in previous combinations within similar domains like robotic manipulation involving contacts.