most citedUnleashing Embodied Task Planning Ability in LLMs via Reinforcement Learning

1 citations · 1 across the 7 of their papers we have counts for

collaborators

11 papers

cs.CL2026

WESR: Scaling and Evaluating Word-level Event-Speech Recognition

Chenchen Yang, Kexin Huang, Liwei Fan +8

Speech conveys not only linguistic information but also rich non-verbal vocal events such as laughing and crying. While semantic transcription is well-studied, the precise localiza…

cs.CV2025

FASTer: Toward Efficient Autoregressive Vision Language Action Modeling via Neural Action Tokenization

Yicheng Liu, Shiduo Zhang, Zibin Dong +12

Autoregressive vision-language-action (VLA) models have recently demonstrated strong capabilities in robotic manipulation. However, their core process of action tokenization often…

cs.RO2025

SRPO: Self-Referential Policy Optimization for Vision-Language-Action Models

Senyu Fei, Siyin Wang, Li Ji +7

Vision-Language-Action (VLA) models excel in robotic manipulation but are constrained by their heavy reliance on expert demonstrations, leading to demonstration bias and limiting p…

cs.RO2025

RoboOmni: Proactive Robot Manipulation in Omni-modal Context

Siyin Wang, Jinlan Fu, Feihong Liu +11

Recent advances in Multimodal Large Language Models (MLLMs) have driven rapid progress in Vision-Language-Action (VLA) models for robotic manipulation. Although effective in many s…

cs.RO2025

LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models

Senyu Fei, Siyin Wang, Junhao Shi +10

Visual-Language-Action (VLA) models report impressive success rates on robotic manipulation benchmarks, yet these results may mask fundamental weaknesses in robustness. We perform…

cs.SD2025

XY-Tokenizer: Mitigating the Semantic-Acoustic Conflict in Low-Bitrate Speech Codecs

Yitian Gong, Luozhijie Jin, Ruifan Deng +6

Speech codecs serve as bridges between speech signals and large language models. An ideal codec for speech language models should not only preserve acoustic information but also ca…