arXiv subjects

Yinxing Chen

Publications and source records attributed to Yinxing Chen.

4 recordsLinked to original sources

arXiv2025

Hybrid Diffusion Policies with Projective Geometric Algebra for Efficient Robot Manipulation Learning

Diffusion policies are a powerful paradigm for robot learning, but their training is often inefficient. A key reason is that networks must relearn fundamental spatial concepts, such as translations and rotations, from scratch for every new task. To alleviate this redundancy, we propose embedding geometric inductive biases directly into the network architecture using Projective Geometric Algebra (PGA). PGA provides a unified algebraic framework for representing geometric primitives and transformations, allowing neural networks to reason about spatial structure more effectively. In this paper, we introduce hPGA-DP, a novel hybrid diffusion policy that capitalizes on these benefits. Our architecture leverages the Projective Geometric Algebra Transformer (P-GATr) as a state encoder and action decoder, while employing established U-Net or Transformer-based modules for the core denoising process. Through extensive experiments and ablation studies in both simulated and real-world environments, we demonstrate that hPGA-DP significantly improves task performance and training efficiency. Notably, our hybrid approach achieves substantially faster convergence compared to both standard diffusion policies and architectures that rely solely on P-GATr. The project website is available at: https://apollo-lab-yale.github.io/26-ICRA-hPGA-website/.

cs.RO↗

arXiv2025

PRISM-DP: Spatial Pose-based Observations for Diffusion-Policies via Segmentation, Mesh Generation, and Pose Tracking

Diffusion policies generate robot motions by learning to denoise action-space trajectories conditioned on observations. These observations are commonly streams of RGB images, whose high dimensionality includes substantial task-irrelevant information, requiring large models to extract relevant patterns. In contrast, using structured observations like the spatial poses of key objects enables training more compact policies with fewer parameters. However, obtaining accurate object poses in open-set, real-world environments remains challenging, as 6D pose estimation and tracking methods often depend on markers placed on objects beforehand or pre-scanned object meshes that require manual reconstruction. We propose PRISM-DP, an approach that leverages segmentation, mesh generation, and pose tracking models to enable compact diffusion policy learning directly from the spatial poses of task-relevant objects. Crucially, by using a mesh generation model, PRISM-DP eliminates the need for manual mesh creation, improving scalability in open-set environments. Experiments in simulation and the real world show that PRISM-DP outperforms high-dimensional image-based policies and achieves performance comparable to policies trained with ground-truth state information.

cs.RO↗

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↗