arXiv subjects

Wenling Li

Publications and source records attributed to Wenling Li.

3 recordsLinked to original sources

arXiv2026

World Pilot: Steering Vision-Language-Action Models with World-Action Priors

Vision-Language-Action (VLA) models inherit semantic grounding from large-scale pretraining and perform competently across in-distribution manipulation tasks. This grounding, however, is built on static image-text pairs, whereas manipulation is a continuous, contact-rich process whose dynamics this pretraining cannot capture. We present World Pilot, a VLA framework that augments the policy with priors from a World-Action Model (WAM), routed into the decision chain through two complementary pathways. Latent Steering conditions the perception layer on a scene-evolution latent, and Action Steering supplies an anticipated trajectory as a motion prior to the action generator. Together the two priors equip the VLA with an anticipated view of the scene and a trajectory-level motion hint alongside its semantic conditioning, and the scene-evolution prior remains effective even when supplied by a video-pretrained world model that has not been action-post-trained. World Pilot attains a state-of-the-art Total success rate of 84.7% on the LIBERO-Plus zero-shot OOD benchmark and the highest success rate on every real-robot setting across four manipulation tasks, with the largest margins under shifts in viewpoint, geometry, deformable state, and pose. Project Website: https://world-pilot.github.io/

cs.RO↗

arXiv2025

EmbodiedCoder: Parameterized Embodied Mobile Manipulation via Modern Coding Model

Recent advances in control robot methods, from end-to-end vision-language-action frameworks to modular systems with predefined primitives, have advanced robots' ability to follow natural language instructions. Nonetheless, many approaches still struggle to scale to diverse environments, as they often rely on large annotated datasets and offer limited interpretability.In this work, we introduce EmbodiedCoder, a training-free framework for open-world mobile robot manipulation that leverages coding models to directly generate executable robot trajectories. By grounding high-level instructions in code, EmbodiedCoder enables flexible object geometry parameterization and manipulation trajectory synthesis without additional data collection or fine-tuning.This coding-based paradigm provides a transparent and generalizable way to connect perception with manipulation. Experiments on real mobile robots show that EmbodiedCoder achieves robust performance across diverse long-term tasks and generalizes effectively to novel objects and environments.Our results demonstrate an interpretable approach for bridging high-level reasoning and low-level control, moving beyond fixed primitives toward versatile robot intelligence. See the project page at: https://embodiedcoder.github.io/EmbodiedCoder/

cs.RO↗

arXiv2014

Kullback-Leibler divergence for interacting multiple model estimation with random matrices

This paper studies the problem of interacting multiple model (IMM) estimation for jump Markov linear systems with unknown measurement noise covariance. The system state and the unknown covariance are jointly estimated in the framework of Bayesian estimation, where the unknown covariance is modeled as a random matrix according to an inverse-Wishart distribution. For the IMM estimation with random matrices, one difficulty encountered is the combination of a set of weighted inverse-Wishart distributions. Instead of using the moment matching approach, this difficulty is overcome by minimizing the weighted Kullback-Leibler divergence for inverse-Wishart distributions. It is shown that a closed form solution can be derived for the optimization problem and the resulting solution coincides with an inverse-Wishart distribution. Simulation results show that the proposed filter performs better than the previous work using the moment matching approach.

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