This study explores the evolving landscape of embodied intelligence, and presents a comprehensive survey on leveraging reinforcement learning (RL) to enhance embodied intelligence, systematically addressing the evolution from single-task performance in controlled settings to sophisticated cross-environment and cross-task capabilities.
This Review synthesizes recent progress in Embodied AI and articulate Intent-Driven Embodied Artificial Intelligence (IDEAI) as a system-level organizing framework in which intent functions as an explicit, revisable, and verifiable mediating construct between human goals, environmental constraints, and agent behavior.
Nanning Zheng· National Science Review· 0 citations
This work proposes ParallelWorld, a multi-horizon test-time scaling framework for embodied reasoning that empowers agents to simulate and evaluate multi-step future trajectories in parallel before committing to an action.
Min Chen, Shengjun Zhang, Yu-Xin Li et al.· 0 citations
This work synthesizes perspectives from philosophy, cognitive science, and AI to define agency, outline its key properties, and situate it in relation to existing paradigms such as reinforcement learning, symbolic reasoning, Belief–Desire–Intention (BDI) architectures, and embodied cognition.
UnifiedPlayers, a cooperative framework comprising a Planning Player that generates tasks, an Execution Player that produces multi-turn trajectories with Python tool calls, and an Evaluation Player that constructs executable verifiers, highlights cooperation among specialized players as a promising path toward self-enh...
Autonomous embodied intelligence has emerged as a promising new paradigm to enable intelligent agents to perceive, reason, and interact with dynamic environments. Recent advances in large language models (LLMs), vision-language models, and multi-agent systems have greatly improved embodied decision-making. But many cur...
K. Jithendra, K. Chowdappa, A. U. M. Reddy et al.· 2026 International Conferenc...· 0 citations
This work proposes SHAPER, a self-evolving framework for train-free embodied adaptation that keeps model parameters frozen and improves the non-parametric agent system by evolving reusable skills and a context-code harness through target-environment rollouts.
