papers

Publications (17)

cs.RO2026

PRM-as-a-Judge 1.5: A Toolkit for Robot Process Assessment

Yuyang Liu, Yanqing Shen, Ruike Chen +22

Fine-grained robotic evaluation matters for understanding embodied models, going beyond binary success rates and rule-based process scores. We present PRM-as-a-Judge 1.5, a toolkit…

cs.RO2026

XPolicyLab: A Unified Standard and Open Ecosystem for Robot Policy Evaluation and Deployment

XPolicyLab Community, Tianxing Chen, Yue Chen +65

Robot policy evaluation and deployment remain fragmented by model-specific software dependencies, data representations, and runtime interfaces, so that connecting N policies to M e…

cs.RO2026

Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels

Xinyu Yang, Tianxing Chen, Honghao Su +38

The paper proposes a layered systems framework for achieving trustworthy embodied intelligence, defining trustworthiness as sustained safe success and introducing graded trustworth…

#trustworthy ai#embodied intelligence#robotics#system safety
cs.RO2026

Data Pyramid for Embodied Manipulation: A Survey

Yifan Ye, Yankai Fu, Yaoxu Lv +26

Multimodal foundation models learned to see and to speak by consuming the whole internet. Embodied agents admit no such shortcut, since they require data that couple observations w…

cs.RO2026

DenseReward: Dense Reward Learning via Failure Synthesis for Robotic Manipulation

Yu Fang, Wanxi Dong, Jiaqi Liu +7

The paper presents DenseReward, a dense visual‑language reward model for robotic manipulation that is trained on automatically synthesized failure trajectories in simulation, enabl…

#dense reward learning#failure synthesis#robotic manipulation#vision-language models
cs.RO2026

RoboDojo: A Unified Sim-and-Real Benchmark for Comprehensive Evaluation of Generalist Robot Manipulation Policies

Tianxing Chen, Yue Chen, Zixuan Li +41

Generalist robot manipulation policies have advanced rapidly, yet existing benchmarks remain limited in systematically evaluating their capabilities. Many rely on simple, short-hor…