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From the 1 of 9 linked papers with an AI index.

collaborators

9 papers

cs.RO2026

WorldSimProbe: Diagnosing Simulator Faithfulness in Action-Conditioned World Models for Embodied Manipulation

Peterson Co, Sicheng Hu, Chunxuan Jiao +17

Action-conditioned world models (ACWMs) promise to provide embodied AI with scalable predictive simulators for planning, policy evaluation, and data generation. Realizing this prom…

cs.RO2026

JoyAI-Sim: A Simulation-Enabled Interconversion Toolchain for the Embodied Data Pyramid

Peidong Liu, Yongce Liu, Songyan Guo +34

JoyAI-Sim is a toolchain that connects real robots, simulation, and human demonstrations to enable scalable evaluation and generation of robot training data using calibrated digita…

cs.LG2026

CDCP: Conditional Diffusion Model with Contextual Prompts for Multi-task Offline Safe Reinforcement Learning

Jiayi Guan, Tianle Zhang, Li Shen +8

Multi-task offline safe reinforcement learning (RL) promises to learn a shared optimal safe policy from offline data across multiple tasks. This paradigm provides an effective mean…

cs.AI2026

Towards Understanding Modality Interaction in Multimodal Language Models via Partial Information Decomposition

Wanlong Fang, Tianle Zhang, Wen Tao +1

Understanding how multimodal large language models use different modalities is important for reliable reasoning. We employ Partial Information Decomposition (PID) as a decision-lev…

cs.RO2026

PACE: Phase-Aware Chunk Execution for Robot Policies with Action Chunking

Junnan Nie, Jiayi Li, Jiachen Zhang +5

Recent vision-language-action and diffusion-based robot policies often use action chunking, where each policy query predicts a sequence of future actions and the robot executes an…

cs.LG2025

PIGDreamer: Privileged Information Guided World Models for Safe Partially Observable Reinforcement Learning

Dongchi Huang, Jiaqi Wang, Yang Li +3

Partial observability presents a significant challenge for Safe Reinforcement Learning (Safe RL), as it impedes the identification of potential risks and rewards. Leveraging specif…