works on

From the 1 of 8 linked papers with an AI index.

collaborators

8 papers

cs.RO2026

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

XPolicyLab Community, Tianxing Chen, Yue Chen +67

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…

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…

cs.CV2026

InfBaGel: Human-Object-Scene Interaction Generation with Dynamic Perception and Iterative Refinement

Yude Zou, Junji Gong, Xing Gao +3

Human-object-scene interactions (HOSI) generation has broad applications in embodied AI, simulation, and animation. Unlike human-object interaction (HOI) and human-scene interactio…

cs.RO2026

ManiTwin: Scaling Data-Generation-Ready Digital Object Dataset to 100K

Kaixuan Wang, Tianxing Chen, Jiawei Liu +13

Learning in simulation provides a useful foundation for scaling robotic manipulation capabilities. However, this paradigm often suffers from a lack of data-generation-ready digital…

cs.RO2026

RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design

Tianxing Chen, Yuran Wang, Mingleyang Li +16

Robotic manipulation policies have made rapid progress in recent years, yet most existing approaches give limited consideration to memory capabilities. Consequently, they struggle…