Artificial intelligence (AI) and especially reinforcement learning (RL) have the potential to
enable agents to learn and perform tasks autonomously with superhuman performance …

Tree perception is an essential building block toward autonomous forestry operations.
Current developments generally consider input data from lidar sensors to solve forest …

Despite its success, Model Predictive Control (MPC) often requires intensive task-specific
engineering and tuning. On the other hand, Reinforcement Learning (RL) architectures …

Humans and robots operating in unstructured environments both need to classify objects
through haptic exploration and use them in various tasks, but currently they differ greatly in …

Introduction This paper presents Enactive Artificial Intelligence (eAI) as a gender-inclusive
approach to AI, emphasizing the need to address social marginalization resulting from …

This article introduces a three-axis framework indicating how AI can be informed by
biological examples of social learning mechanisms. We argue that the complex human …

Symbolic planning is a powerful technique to solve complex tasks that require long
sequences of actions and can equip an intelligent agent with complex behavior. The …

Federated learning (FL) has emerged as a promising approach to collaboratively train
machine learning models across multiple edge devices while preserving privacy. The …

具身智能强调智能受脑, 身体与环境协同影响, 更侧重关注智能体与环境的“交互”. 因此,
在具身智能的研究中, 智能体的物理形态与感知, 学习, 控制的关系起到至关重要的作用. 当前 …

In control theory, reactive methods have been widely celebrated owing to their success in
providing robust, provably convergent solutions to control problems. Even though such …