Enabling a high-degree-of-freedom robot to learn specific skills is a challenging task due to the complexity of robotic dynamics. Reinforcement learning (RL) has emerged as a promising solution; however, addressing such problems requires the design of multiple reward functions to account for various constraints in robotic motion. Existing approaches typically sum all reward components indiscriminately to optimize the RL value function and policy. We argue that this uniform inclusion of all reward components in policy optimization is inefficient and limits the robot's learning performance. To address this, we propose an Automated Hybrid Reward Scheduling (AHRS) framework based on Large Language Models (LLMs). This paradigm dynamically adjusts the learning intensity of each reward component throughout the policy optimization process, enabling robots to acquire skills in a gradual and structured manner. Specifically, we design a multi-branch value network, where each branch corresponds to a distinct reward component. During policy optimization, each branch is assigned a weight that reflects its importance, and these weights are automatically computed based on rules designed by LLMs. The LLM generates a rule set in advance, derived from the task description, and during training, it selects a weight calculation rule from the library based on language prompts that evaluate the performance of each branch. Experimental results demonstrate that the AHRS method achieves an average 6.48% performance improvement across multiple high-degree-of-freedom robotic tasks.
Unsupervised Skill Discovery for Robotic Manipulation through Automatic Task Generation
NAVER LABS Europe（NAVER欧洲实验室）；TU Darmstadt（达姆施塔特工业大学）；EPITA Research Laboratory (LRE)（EPITA研究实验室）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Center for Cognitive Science, TU Darmstadt（达姆施塔特工业大学认知科学中心）；Hessian Center for Artificial Intelligence (Hessian.ai)（黑森州人工智能中心）
Never-Ending Behavior-Cloning Agent for Robotic Manipulation
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences（机器人国家重点实验室，沈阳自动化研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；School of Automation Science and Engineering, South China University of Technology（自动化科学与工程学院，华南理工大学）；Mohamed bin Zayed University of Artificial Intelligence（马斯达尔大学人工智能学院）
Autonomous Integration and Improvement of Robotic Assembly using Skill Graph Representations
Department of Electrical and Computer Engineering, Carnegie Mellon University（卡内基梅隆大学电气与计算机工程系）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）
Skill-Aware Diffusion for Generalizable Robotic Manipulation
School of Control Science and Engineering, Shandong University（控制科学与工程学院，山东大学）；Department of Computer Science, The University of Manchester（计算机科学系，曼彻斯特大学）
Robotic Grinding Skills Learning Based on Geodesic Length Dynamic Motion Primitives
State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Department of Mechanical Science and Engineering, Huazhong University of Science and Technology（智能制造装备与技术国家重点实验室，机械科学与工程学院，华中科技大学）
Bootstrapping Robotic Skill Learning With Intuitive Teleoperation: Initial Feasibility Study
The Chinese University of Hong Kong（香港中文大学）；Multi-Scale Medical Robotics Center（多尺度医疗机器人中心）；Technical University of Munich（慕尼黑工业大学）；Cornerstone Robotics Limited（基石机器人有限公司）
Dense-Jump Flow Matching with Non-Uniform Time Scheduling for Robotic Policies: Mitigating Multi-Step Inference Degradation
Dept. Computing, Imperial College London（帝国理工学院伦敦分校计算机系）；Dept. Computing, Manchester Metropolitan University（曼彻斯特 Metropolitan 大学计算机系）；CVSSP, University of Surrey（萨里大学CVSSP研究中心）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Institute for Transport Studies, the University of Leeds（利兹大学交通研究所）
Uni-Skill: Building Self-Evolving Skill Repository for Generalizable Robotic Manipulation
Key Laboratory of AI Safety of CAS（中国科学院人工智能安全重点实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Tactile Active Inference Reinforcement Learning for Efficient Robotic Manipulation Skill Acquisition
Research Center for Intelligent Robotics, School of Astronautics, Northwestern Polytechnical University（西北工业大学航天学院智能机器人研究中心）；National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University（西北工业大学航空航天飞行动力学全国重点实验室）
Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation
Nanjing University（南京大学）；National University of Singapore（新加坡国立大学）；University of Science and Technology of China（中国科学技术大学）；University of California, Berkeley（加州大学伯克利分校）；Carnegie Mellon University（卡内基梅隆大学）
DROM: A Language-Guided Diffusion Framework for Multi-Skill Robotic Manipulation
University of Applied Science and Arts of Southern Switzerland (SUPSI)（瑞士南部应用科学与艺术大学）；Institute of Systems and Technologies for Sustainable Production (ISTePS)（可持续生产系统与技术研究所）；Istituto Dalle Molle di studi sull’intelligenza artificiale (IDSIA)（达勒莫尔人工智能研究所）
Decompose and Recompose: Reasoning New Skills from Existing Abilities for Cross-Task Robotic Manipulation
School of Artificial Intelligence, College of Intelligence and Computing, Tianjin University, China（人工智能学院，智能与计算学院，天津大学，中国）；School of Computer Science and Information Engineering, Hefei University of Technology, China（计算机科学与信息工程学院，合肥工业大学，中国）
本文提出Decompose and Recompose框架，通过分解现有任务演示为可解释的技能-动作对，实现跨任务机械操作的零样本泛化。
