Efficient Gradient-Based Inference for Manipulation Planning in Contact Factor Graphs

2025-03-08

AI Summary

This work addresses the challenges of geometric-physical coupling modeling and efficient dynamic constraint reasoning in contact-rich robotic manipulation planning. We propose the Contact Factor Graph (CFG) framework, which models diverse contact phenomena as differentiable factors and constructs a sparse factor graph leveraging conditional independencies induced by contact structure. By unifying contact mechanics, differentiable optimization, and gradient-based probabilistic inference, CFG enables end-to-end trajectory generation and posterior distribution approximation. Compared to conventional sampling-based or non-differentiable methods, CFG significantly improves both contact inference accuracy and optimization efficiency. It robustly generates physically feasible trajectories across multiple manipulation tasks—including grasping, pushing, and assembly—while enabling explicit differentiable modeling of contact relationships and joint probabilistic inference for the first time.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper presents a framework designed to tackle a range of planning problems arise in manipulation, which typically involve complex geometric-physical reasoning related to contact and dynamic constraints. We introduce the Contact Factor Graph (CFG) to graphically model these diverse factors, enabling us to perform inference on the graphs to approximate the distribution and sample appropriate solutions. We propose a novel approach that can incorporate various phenomena of contact manipulation as differentiable factors, and develop an efficient inference algorithm for CFG that leverages this differentiability along with the conditional probabilities arising from the structured nature of contact. Our results demonstrate the capability of our framework in generating viable samples and approximating posterior distributions for various manipulation scenarios.

Problem

• Addresses manipulation planning with contact and dynamic constraints
• Introduces Contact Factor Graph for geometric-physical reasoning
• Develops efficient inference algorithm for differentiable contact factors

Innovation

• Contact Factor Graph models manipulation factors
• Differentiable factors enable efficient gradient-based inference
• Algorithm leverages conditional probabilities for solution sampling