Implicit Contact Diffuser: Sequential Contact Reasoning with Latent Point Cloud Diffusion (2410.16571v1)

Abstract: Long-horizon contact-rich manipulation has long been a challenging problem, as it requires reasoning over both discrete contact modes and continuous object motion. We introduce Implicit Contact Diffuser (ICD), a diffusion-based model that generates a sequence of neural descriptors that specify a series of contact relationships between the object and the environment. This sequence is then used as guidance for an MPC method to accomplish a given task. The key advantage of this approach is that the latent descriptors provide more task-relevant guidance to MPC, helping to avoid local minima for contact-rich manipulation tasks. Our experiments demonstrate that ICD outperforms baselines on complex, long-horizon, contact-rich manipulation tasks, such as cable routing and notebook folding. Additionally, our experiments also indicate that \methodshort can generalize a target contact relationship to a different environment. More visualizations can be found on our website $\href{https://implicit-contact-diffuser.github.io/}{https://implicit-contact-diffuser.github.io}$

• The paper presents the Implicit Contact Diffuser that integrates neural descriptor fields with MPC to overcome local minima in contact-rich tasks.
• It employs a latent diffusion model that efficiently predicts a sequence of neural descriptor subgoals for guiding manipulation in complex, long-horizon tasks.
• Empirical results in cable routing and notebook folding demonstrate improved success rates compared to traditional keypoint-based and conventional MPC methods.

Implicit Contact Diffuser: Sequential Contact Reasoning with Latent Point Cloud Diffusion

The paper "Implicit Contact Diffuser: Sequential Contact Reasoning with Latent Point Cloud Diffusion" addresses the complex issue of contact-rich manipulation in robotics, a prominent challenge that requires the integration of discrete contact modes with continuous object motions. The authors propose a novel framework, leveraging a diffusion-based model, which significantly advances the manipulation of deformable objects through sequential reasoning of contact states.

Technical Contributions

The core contribution of the paper is the Implicit Contact Diffuser (ICD), a learning-based model that integrates a neural descriptor framework with a model predictive control (MPC) method. The proposed ICD model generates neural descriptors that guide the MPC by representing the contact relationships between objects and their environment. This method is novel in that it facilitates overcoming local minima challenges typically faced in contact-rich tasks.

1. Neural Descriptor Fields (NDF): The authors enhance traditional Neural Descriptor Fields to capture task-relevant geometric relationships. These descriptors encode both occupancy and the gradient direction of the signed distance function, thus providing a dense representation of contact relationships. This enables a more generalized task execution across different environments.
2. Latent Diffusion Model: The paper introduces a latent diffusion model that predicts a sequence of contact states, represented by neural descriptors. By requiring only low-dimensional latent vectors for prediction, the method efficiently generates subgoals to direct MPC.
3. Reachability Analysis: To optimize the number of generated subgoals, the authors incorporate a reachability function that evaluates task-specific sequence lengths, ensuring efficient and effective task execution.

Empirical Evaluation

The authors validate ICD using complex, long-horizon manipulation tasks such as cable routing and notebook folding, demonstrating its superiority over various baseline methods. The ICD consistently achieved higher success rates in comparison to keypoint-based and conventional MPC methods, underscoring the advantages of its implicit, contact-oriented state representation.

• Cable Routing and Notebook Folding: In the experiments conducted in simulation environments, the ICD exhibits robust performance in maintaining desired contact relationships with deformable objects. Notably, ICD outperformed baseline strategies significantly in terms of successfully navigating long-horizon tasks with contact switches.
• Adaptability: The paper also explores ICD’s capacity to adapt a pre-specified contact goal to different environmental configurations, showcasing its potential for practical applications where exact goal specification may be unavailable or impractical.

Broader Implications and Future Directions

The research presents significant theoretical implications by integrating diffusion models with implicit neural representation, advancing the dialogue on contact-rich manipulation in robotic systems. Practically, the ICD framework can inspire development in fields requiring precise contact handling, such as surgical robotics or automated assembly lines.

Future work could focus on addressing limitations such as the reliance on full point cloud data, potentially integrating advanced perception techniques for real-world applicability. Additionally, incorporating real-time learning aspects to handle dynamic environmental changes and uncertainties could further enhance the framework's robustness and adaptability.

In conclusion, this paper presents a substantial contribution to the robotic manipulation landscape, offering a compelling approach to solving intricate contact-rich tasks with long-horizon reasoning capabilities. The ICD framework sets the stage for future innovations, encouraging ongoing exploration into efficient, intelligent, and adaptable robotic systems.