Hugging Face Trending Papers
Causal world models under physical parameter shifts
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arXiv Machine Learning
Oct 22
Causal world models under physical parameter shifts
The paper investigates how causal modeling can improve the robustness of world models to physical parameter shifts. By freezing a dependency mask derived from paired interventions, the authors reduce out‑of‑distribution RMSE on a synthetic benchmark from 1.769 to 0.697, nearly matching an oracle mask. In a robotic link‑mass extrapolation task, incorporating explicit affine coefficients for mass effects and using a known PD controller yields a consistent predictive gain, raising median joint R² from 0.609 to 0.727 across all runs, while a learned‑torque model competes with the best recurrent world model when out‑of‑range masses are supplied.
arXiv AI
Oct 23
Feedback World Model Enables Precise Guidance of Diffusion Policy
The paper introduces a feedback world model that updates predictions online using the robot’s observed next state after each action, thereby closing the loop between prediction and observation. This lightweight feedback state corrects future predictions without extra training or parameter updates, and the authors provide convergence guarantees under bounded dynamics residuals. Experiments on LIBERO‑Plus, Robomimic, and real‑world tasks show significant reductions in prediction error (up to 76.4%) and improved policy performance, even when applied to pretrained foundation models like Fast‑WAM‑Joint.
arXiv AI
Oct 15
RAP-WAM: Learning World-Action Models through Predictive Rehearsal and Action Perturbations
arXiv:2610.16443v1 Announce Type: new Abstract: Recent world action models draw on video generation priors to jointly predict future observations and robot actions. Adapting these priors to control r...
arXiv AI
Aug 24
ForeTime-VLA: Causal Future-Token Distillation from a World Action Model for Conveyor-Belt Manipulation
ForeTime‑VLA is a causal vision‑language‑action policy that distills future‑aware representations from a frozen Fast‑WAM teacher, enabling it to anticipate contact events during conveyor‑belt manipulation. The method compresses current and future video latents into a 64‑dimensional target, uses an eight‑frame history encoder to predict this target along with manipulation phase and time‑to‑transition, and conditions a VLM prefix on future tokens and phase. On a deduplicated conveyor‑belt dataset, ForeTime‑VLA reduces test MAE by 2.63% and L2 by 3.02%, while real‑robot experiments show significantly higher grasp success rates compared to the next‑best reference. whyItMatters":"The approach demonstrates that distilling future‑token knowledge from a world‑action model can improve dynamic manipulation performance without the computational cost of running the teacher at inference time."
arXiv Computer Vision
Oct 8
Long-WAM: Scaling the Context of World-Action Models
arXiv:2610.10528v1 Announce Type: cross Abstract: Real-time robot control demands enough visual history to infer motion and task progress, but processing that history can delay action. We present Lon...
arXiv AI
Sep 2
IMPACT: Attention Is the Interaction Map for Scalable Interaction-Aware World Model Training
arXiv:2609.00161v1 Announce Type: new Abstract: World models have made remarkable progress in action-conditioned future prediction for embodied agents, yet still struggle to model physically plausibl...