Tootfinder

Time-Unified Diffusion Policy with Action Discrimination for Robotic Manipulation
Ye Niu, Sanping Zhou, Yizhe Li, Ye Den, Le Wang
https://arxiv.org/abs/2506.09422

Time-Unified Diffusion Policy with Action Discrimination for Robotic Manipulation
In many complex scenarios, robotic manipulation relies on generative models to estimate the distribution of multiple successful actions. As the diffusion model has better training robustness than other generative models, it performs well in imitation learning through successful robot demonstrations. However, the diffusion-based policy methods typically require significant time to iteratively denoise robot actions, which hinders real-time responses in robotic manipulation. Moreover, existing dif…

The @… paper Algorithmic Alphabets for Patterns of Movement by @… @… me is published !

Low-Barrier Dataset Collection with Real Human Body for Interactive Per-Garment Virtual Try-On
Zaiqiang Wu, Yechen Li, Jingyuan Liu, Yuki Shibata, Takayuki Hori, I-Chao Shen, Takeo Igarashi
https://arxiv.org/abs/2506.10468

Low-Barrier Dataset Collection with Real Human Body for Interactive Per-Garment Virtual Try-On
Existing image-based virtual try-on methods are often limited to the front view and lack real-time performance. While per-garment virtual try-on methods have tackled these issues by capturing per-garment datasets and training per-garment neural networks, they still encounter practical limitations: (1) the robotic mannequin used to capture per-garment datasets is prohibitively expensive for widespread adoption and fails to accurately replicate natural human body deformation; (2) the synthesized …

Chain-of-Action: Trajectory Autoregressive Modeling for Robotic Manipulation
Wenbo Zhang, Tianrun Hu, Yanyuan Qiao, Hanbo Zhang, Yuchu Qin, Yang Li, Jiajun Liu, Tao Kong, Lingqiao Liu, Xiao Ma
https://arxiv.org/abs/2506.09990

Chain-of-Action: Trajectory Autoregressive Modeling for Robotic Manipulation
We present Chain-of-Action (CoA), a novel visuo-motor policy paradigm built upon Trajectory Autoregressive Modeling. Unlike conventional approaches that predict next step action(s) forward, CoA generates an entire trajectory by explicit backward reasoning with task-specific goals through an action-level Chain-of-Thought (CoT) process. This process is unified within a single autoregressive structure: (1) the first token corresponds to a stable keyframe action that encodes the task-specific goals…

Periodic Bipedal Gait Learning Using Reward Composition Based on a Novel Gait Planner for Humanoid Robots
Bolin Li, Linwei Sun, Xuecong Huang, Yuzhi Jiang, Lijun Zhu
https://arxiv.org/abs/2506.08416

Periodic Bipedal Gait Learning Using Reward Composition Based on a Novel Gait Planner for Humanoid Robots
This paper presents a periodic bipedal gait learning method using reward composition, integrated with a real-time gait planner for humanoid robots. First, we introduce a novel gait planner that incorporates dynamics to design the desired joint trajectory. In the gait design process, the 3D robot model is decoupled into two 2D models, which are then approximated as hybrid inverted pendulums (H-LIP) for trajectory planning. The gait planner operates in parallel in real time within the robot's lea…

On the train to Barcelona for @… ! Having already spent a couple of days surrounded by live coders in a Peak District retreat I've got this weird thing where I keep seeing live coders just walking around everywhere who turn out to be lookalikes
I'll be involved with a paper about choreographic notation for robots with @…

FreqPolicy: Frequency Autoregressive Visuomotor Policy with Continuous Tokens
Yiming Zhong, Yumeng Liu, Chuyang Xiao, Zemin Yang, Youzhuo Wang, Yufei Zhu, Ye Shi, Yujing Sun, Xinge Zhu, Yuexin Ma
https://arxiv.org/abs/2506.01583

FreqPolicy: Frequency Autoregressive Visuomotor Policy with Continuous Tokens
Learning effective visuomotor policies for robotic manipulation is challenging, as it requires generating precise actions while maintaining computational efficiency. Existing methods remain unsatisfactory due to inherent limitations in the essential action representation and the basic network architectures. We observe that representing actions in the frequency domain captures the structured nature of motion more effectively: low-frequency components reflect global movement patterns, while high-…