@article{duan2026dsd,title={Direction-Scale Decomposition in Action Representation: Rethinking What to Tokenize for Vision-Language-Action Models},author={Duan, Yufei and Yin, Hang and Longhini, Alberta and Tang, Chao and Kragic, Danica},year={2026},journal={arXiv preprint arXiv:2609.28865},eprint={2609.28865},archiveprefix={arXiv},primaryclass={cs.CV},url={https://arxiv.org/abs/2609.28865},}
2025
Real-Time Iteration Scheme for Diffusion Policy
Yufei Duan, Hang Yin, and Danica Kragic
2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025
Diffusion Policies have demonstrated impressive performance in robotic manipulation tasks. However, their long inference time, resulting from an extensive iterative denoising process, and the need to execute an action chunk before the next prediction to maintain consistent actions limit their applicability to latency-critical tasks or simple tasks with a short cycle time. While recent methods explored distillation or alternative policy structures to accelerate inference, these often demand additional training, which can be resource-intensive for large robotic models. In this paper, we introduce a novel approach inspired by the Real-Time Iteration (RTI) Scheme, a method from optimal control that accelerates optimization by leveraging solutions from previous time steps as initial guesses for subsequent iterations. We explore the application of this scheme in diffusion inference and propose a scaling-based method to effectively handle discrete actions, such as grasping, in robotic manipulation. The proposed scheme significantly reduces runtime computational costs without the need for distillation or policy redesign. This enables a seamless integration into many pre-trained diffusion-based models, in particular, to resource-demanding large models. We also provide theoretical conditions for the contractivity which could be useful for estimating the initial denoising step. Quantitative results from extensive simulation experiments show a substantial reduction in inference time, with comparable overall performance compared with Diffusion Policy using full-step denoising.
2024
Energy-Optimized Planning in Non-Uniform Wind Fields with Fixed-Wing Aerial Vehicles
Yufei Duan*, Florian Achermann*, Jaeyoung Lim, and 1 more author
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024
Fixed-wing small uncrewed aerial vehicles (sUAVs) possess the capability to remain airborne for extended durations and traverse vast distances. However, their operation is susceptible to wind conditions, particularly in regions of complex terrain where high wind speeds may push the aircraft beyond its operational limitations, potentially raising safety concerns. Moreover, wind impacts the energy required to follow a path, especially in locations where the wind direction and speed are not favorable. Incorporating wind information into mission planning is essential to ensure both safety and energy efficiency. In this paper, we propose a sampling-based planner using the kinematic Dubins aircraft paths with respect to the ground, to plan energy-efficient paths in non-uniform wind fields. We study the planner characteristics with synthetic and real-world wind data and compare its performance against baseline cost and path formulations. We demonstrate that the energy-optimized planner effectively utilizes updrafts to minimize energy consumption, albeit at the expense of increased travel time. The ground-relative path formulation facilitates the generation of safe trajectories onboard sUAVs within reasonable computational timeframes.
Energy-Optimized Planning in Non-Uniform Wind Fields with Fixed-Wing Aerial Vehicles2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024