arXiv Computer Vision
Ada3Drift: Adaptive Training-Time Drifting for One-Step 3D Visuomotor Robotic Manipulation
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arXiv AI
Jun 9
Latent Diffusion Policy: Shaping Latent Spaces for Diffusion-Based Robotic Manipulation
arXiv:2606. 08657v1 Announce Type: cross Abstract: Diffusion-based visuomotor policies operating directly in raw action spaces conflate scene comprehension with trajectory generation within a single denoising process.
arXiv AI
Jul 17
RoboTTT: Context Scaling for Robot Policies
arXiv:2607. 15275v1 Announce Type: cross Abstract: Recent robot foundation models operate with single-step or short-history visuomotor context.
arXiv AI
Jun 2
Implicit Drifting Policy: One-Step Action Generation via Conditional Expert Geometry
arXiv:2606. 01098v1 Announce Type: cross Abstract: Generative action policies based on diffusion or flow matching excel in behavior cloning, yet their iterative sampling is prohibitive for high-frequency robot control.
arXiv Machine Learning
Sep 16
TEMPO: Learning Temporal Context for Dynamic Robot Manipulation
arXiv:2609.16864v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have achieved impressive performance in quasi-static manipulation, but struggle in dynamic manipulation tasks bec...
arXiv Computer Vision
Sep 18
Learning Foresight without Explicit Trajectories for 3D Diffusion Policies
The paper introduces Movement Trend Guidance, a method that equips 3D diffusion policies with foresight by learning a compact latent representation of interaction evolution from a brief observation history. This latent, supervised by sparse future gripper states during training, serves as future-oriented conditioning during inference, enhancing action generation without adding explicit planning. The approach improves performance on RoboTwin2.0, LIBERO-40, and DexArt benchmarks, achieving higher success rates across multiple tasks.
arXiv AI
Sep 23
HybridFlow: A 2-NFE Generative Policy for Real-Time Robotic Manipulation
HybridFlow is a generative policy for robotic manipulation that uses a three‑stage inference procedure requiring only two network function evaluations (2‑NFE). The policy first generates a coarse action trajectory with a Global Jump based on MeanFlow, then refines the state using a parameter‑free ReNoise interpolation, and finally performs a Local Refine to query the instantaneous‑velocity limit. Experiments on RoboMimic and five real‑robot settings show that HybridFlow achieves high success rates and improves task performance over a 16‑step Diffusion Policy while reducing action‑generation latency by roughly eightfold.