Adaptive Diffusion Constrained Sampling for Bimanual Robot Manipulation

2025-05-19

AI Summary

In dual-arm robotic cooperative manipulation, simultaneously satisfying multiple geometric constraints—both equality and inequality—in high-dimensional configuration spaces remains challenging due to low sampling efficiency and poor generalization. Method: This paper proposes the Adaptive Dynamic Constraint Sampling (ADCS) framework. It introduces a Transformer-based dynamic constraint weighting mechanism for context-aware constraint importance modeling; integrates Lie-algebraic pose-difference representation, Signed Distance Function (SDF)-encoded geometric constraints, and constraint embedding learning into an energy-based diffusion model; and designs a two-stage Langevin dynamics sampler with density-aware reweighting. Contribution/Results: Experiments demonstrate that ADCS significantly improves sampling diversity and cross-task generalization, while outperforming state-of-the-art methods in real-time performance, accuracy, and robustness.

Technology Category

• Intelligent Robots: Manipulation
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Coordinated multi-arm manipulation requires satisfying multiple simultaneous geometric constraints across high-dimensional configuration spaces, which poses a significant challenge for traditional planning and control methods. In this work, we propose Adaptive Diffusion Constrained Sampling (ADCS), a generative framework that flexibly integrates both equality (e.g., relative and absolute pose constraints) and structured inequality constraints (e.g., proximity to object surfaces) into an energy-based diffusion model. Equality constraints are modeled using dedicated energy networks trained on pose differences in Lie algebra space, while inequality constraints are represented via Signed Distance Functions (SDFs) and encoded into learned constraint embeddings, allowing the model to reason about complex spatial regions. A key innovation of our method is a Transformer-based architecture that learns to weight constraint-specific energy functions at inference time, enabling flexible and context-aware constraint integration. Moreover, we adopt a two-phase sampling strategy that improves precision and sample diversity by combining Langevin dynamics with resampling and density-aware re-weighting. Experimental results on dual-arm manipulation tasks show that ADCS significantly improves sample diversity and generalization across settings demanding precise coordination and adaptive constraint handling.

Problem

• Coordinated multi-arm manipulation with geometric constraints
• Integrating equality and inequality constraints in diffusion models
• Improving sample diversity and precision in dual-arm tasks

Innovation

• Energy-based diffusion model integrates equality and inequality constraints
• Transformer architecture weights constraint-specific energy functions dynamically
• Two-phase sampling combines Langevin dynamics with resampling for precision