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From the 2 of 7 linked papers with an AI index.

collaborators

7 papers

cs.LG2026

Critic-Free Pretraining for Efficient Online Reinforcement Learning Fine-Tuning

Daoyi Li, Yixian Zhang, Chao Yu +2

Offline-to-online (O2O) reinforcement learning aims to leverage policies pretrained on static datasets while improving them through online interaction. However, directly reusing an…

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…

cs.CV2026

PerceptDrive: Perception Prior World-Action Modeling with Adaptive Expert Routing for End-to-End Autonomous Driving

Yushan Liu, Tianxiong Lv, Bohua Wang +11

Frozen perception foundation models encode rich geometric, semantic, and dynamic knowledge. Yet narrow conditioning interfaces may attenuate task-relevant cues, while static fusion…

cs.RO2026

Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agents

Yixian Zhang, Huanming Zhang, Feng Gao +13

The paper introduces Harness VLA, a memory-augmented framework that combines a frozen vision‑language‑action model with a small set of analytic manipulation primitives to improve r…

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…