arXiv subjects

Francis Fan

Publications and source records attributed to Francis Fan.

2 recordsLinked to original sources

arXiv2025

Dynamic Rank Adjustment in Diffusion Policies for Efficient and Flexible Training

Diffusion policies trained via offline behavioral cloning have recently gained traction in robotic motion generation. While effective, these policies typically require a large number of trainable parameters. This model size affords powerful representations but also incurs high computational cost during training. Ideally, it would be beneficial to dynamically adjust the trainable portion as needed, balancing representational power with computational efficiency. For example, while overparameterization enables diffusion policies to capture complex robotic behaviors via offline behavioral cloning, the increased computational demand makes online interactive imitation learning impractical due to longer training time. To address this challenge, we present a framework, called DRIFT, that uses the Singular Value Decomposition to enable dynamic rank adjustment during diffusion policy training. We implement and demonstrate the benefits of this framework in DRIFT-DAgger, an imitation learning algorithm that can seamlessly slide between an offline bootstrapping phase and an online interactive phase. We perform extensive experiments to better understand the proposed framework, and demonstrate that DRIFT-DAgger achieves improved sample efficiency and faster training with minimal impact on model performance. The project website is available at: https://apollo-lab-yale.github.io/25-RSS-DRIFT-website/.

cs.RO↗

arXiv2024

Optimizing Active Perception for Learning Simultaneous Viewpoint Selection and Manipulation with Diffusion Policy

Robotic manipulation tasks often rely on static cameras for perception, which can limit flexibility, particularly in scenarios like robotic surgery and cluttered environments where mounting static cameras is impractical. Ideally, robots could jointly learn a policy for dynamic viewpoint and manipulation. However, dynamic viewpoint control requires additional degrees of freedom and intricate coordination with manipulation, which results in more challenging policy learning than single-arm manipulation. To address this complexity, we propose an integrated learning framework that combines diffusion policy with a novel look-at inverse kinematics solver for active perception. Our framework helps better coordinating between perception and manipulation. It automatically optimizes camera orientation for viewpoint selection, while allowing the policy to focus on essential manipulation and positioning decisions. We demonstrate that our integrated approach achieves superior performance and learning efficiency compared to directly applying diffusion policies to configuration space or end-effector space with various rotation representations. Further analysis suggests that these performance differences are driven by inherent variations in the high-frequency components across different state-action spaces.

cs.RO↗