Adaptive Diffusion Constrained Sampling for Bimanual Robot Manipulation (2505.13667v2)

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.

Overview of Adaptive Diffusion Constrained Sampling for Bimanual Robot Manipulation

The paper proposes an innovative methodology called Adaptive Diffusion Constrained Sampling (ADCS), aimed at addressing the complex challenges of bimanual robot manipulation in high-dimensional environments. Coordinated multi-arm manipulation inherently requires the satisfaction of multiple geometric constraints that traditional planning and control methodologies struggle to manage effectively. The ADCS framework integrates sophisticated generative models to navigate these complexities, focusing primarily on geometric constraints both in task and configuration spaces.

Core Contributions

The mainstay of the ADCS approach is its generative framework that leverages energy-based models integrated into diffusion processes. It adopts a novel mechanism combining equality constraints, represented in Lie algebra space, and inequality constraints through Signed Distance Functions (SDFs). This dual representation allows for a rich and nuanced understanding of constraints pertinent to robotic manipulation tasks. The paper highlights a particular focus on bimanual manipulation tasks that require precise coordination across multiple end-effectors, such as transport and assembly applications.

Technical Innovations

1. Energy-Based Models in Diffusion Framework: ADCS uses energy-based models that utilize score matching within the context of diffusion models, allowing for precise sampling driven by constraint satisfaction. By using noise injection both in training and sampling phases, the model maintains high generalization capability across diverse configurations and constraints.
2. Dynamic Constraint Integration via Transformer-Based Architecture: A pivotal aspect of ADCS is a Transformer-based architecture that dynamically weights constraint-specific energy functions during inference. This adaptive methodology enables robust integration of potentially conflicting constraints, facilitating task-specific adjustment without the need for manual tuning.
3. Sequential Sampling Strategy: The ADCS approach employs a sophisticated sampling strategy that enhances precision and sample diversity. By combining Langevin dynamics with density-aware resampling, the framework ensures convergence to the constrained regions efficiently, outperforming traditional sampling methodologies.

Experimental Evaluation

The ADCS framework demonstrates substantial improvements in sample diversity and constraint satisfaction in complex dual-arm manipulation scenarios. Extensive experiments were conducted on simulated and real-world robot systems, including collaborative tasks with multiple Franka Emika Panda arms and a bimanual TIAGo robot. Across various settings, ADCS consistently outperformed baseline approaches in terms of task success, sampling efficiency, generalization to novel scenes, and constraint satisfaction.

Implications and Future Directions

The ADCS framework offers promising applications in autonomous robotics, particularly in human environments where robots need to coordinate multiple arms to perform complex tasks. The Transformer-based weighting mechanism introduces a potential paradigm shift in how constraints are managed in robotic systems, allowing for dynamic adaptation based on task context. The ability to learn constraint embeddings through SDFs further extends the applicability of ADCS to environments with complex geometries and interactions.

For future developments, the intersection of ADCS with AI-driven task interpretation, where constraints and environmental interactions are inferred from high-level instructions or goals, could offer meaningful advancements in autonomous system design. Additionally, expanding this approach to incorporate learning from demonstrations or explorative strategies could further improve its applicability in dynamic real-world scenarios.

In conclusion, ADCS presents a compelling framework that not only advances constraint satisfaction methods in robotic manipulation but also offers a robust basis for tackling high-dimensional control problems typically faced by modern autonomous systems. The flexibility and efficiency of the solution foster new opportunities for innovation in AI-driven robotics and complex system planning.