Automating long-horizon tasks with a robotic arm has been a central research topic in robotics. Optimization-based action planning is an efficient approach for creating an action plan to complete a given task. Construction of a reliable planning method requires a design process of conditions, e.g., to avoid collision between objects. The design process, however, has two critical issues: 1) iterative trials--the design process is time-consuming due to the trial-and-error process of modifying conditions, and 2) manual redesign--it is difficult to cover all the necessary conditions manually. To tackle these issues, this paper proposes a future-predictive success-or-failure-classification method to obtain conditions automatically. The key idea behind the proposed method is an end-to-end approach for determining whether the action plan can complete a given task instead of manually redesigning the conditions. The proposed method uses a long-horizon future-prediction method to enable success-or-failure classification without the execution of an action plan. This paper also proposes a regularization term called transition consistency regularization to provide easy-to-predict feature distribution. The regularization term improves future prediction and classification performance. The effectiveness of our method is demonstrated through classification and robotic-manipulation experiments.
AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation
NVIDIA（英伟达）；University of Washington（华盛顿大学）；Universidad Católica San Pablo（圣巴勃罗天主教大学）；MIT（麻省理工学院）；Nanyang Technological University（南洋理工大学）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）
DenseReward: Dense Reward Learning via Failure Synthesis for Robotic Manipulation
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Carnegie Mellon University（卡内基梅隆大学）；Shanghai Jiao Tong University（上海交通大学）；Amazon AWS AI（亚马逊AWS人工智能）
LIBERO-RECOVER: Beyond Task Success Towards Failure Recovery in Robotic Manipulation Models
School of Information and Communication Engineering（信息与通信工程学院）；Dalian University of Technology（大连理工大学）；Beta Infinity（贝塔无限）；Nanyang Technological University（南洋理工大学）；Beijing Jiaotong University（北京交通大学）
ReTrace: Cross-Stage Failure Attribution for Self-Improving Robotic Manipulation
College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机与人工智能学院）；Institute of Trustworthy Embodied AI, Fudan University（复旦大学可信具身人工智能研究院）；Singapore Management University（新加坡管理大学）
FLARE: A Failure-Aware Framework for Autonomous Correction and Recovery in Visual-Language Robotic Manipulation
The Chinese University of Hong Kong（香港中文大学）；Centre for Perceptual and Interactive Intelligence（感知与交互智能中心）；Duke University（杜克大学）；Sun Yat-sen University（中山大学）；TengenX（天工科技）；Tencent Robotics X（腾讯Robotics X实验室）；Shenzhen Loop Area Institute（深圳河套学院）；Guangdong Key Laboratory of Big Data Analysis and Processing（广东省大数据分析与处理重点实验室）
Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation
Shanghai AI Laboratory（上海人工智能实验室）；School of Software & Microelectronics, Peking University（北京大学软件与微电子学院）；Chinese University of Hong Kong（香港中文大学）；National Engineering Research Center for Software Engineering, Peking University（北京大学国家软件工程研究中心）；CFCS, School of CS, Peking University（北京大学计算机科学学院CFCS）
No Plan, Yet Human: A Reactive Robotics Model Predicts Human Planning Failures on a Clinical Task
Robotics and Biology Laboratory, Technische Universität Berlin, Germany（技术大学柏林机器人与生物学实验室，德国）；Science of Intelligence, Research Cluster of Excellence, Berlin, Germany（智能科学，卓越研究集群，柏林，德国）；Robotics Institute Germany（德国机器人研究所）
Collective Learning for Unified Robotic Manipulation via Predictive Dynamics Adaptation
School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；School of Science and Technology, Nanjing University (Suzhou Campus)（南京大学（苏州校区）科学技术学院）
A Transferable Legged Mobile Manipulation Framework Based on Disturbance Predictive Control
Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences（中国科学院机器人与智能制造创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Westlake University（西湖大学）
Results of the Survey: Failures in Robotics and Intelligent Systems
Research Institute for Cognition and Robotics (CoR-Lab)（认知与机器人研究所）；Center of Excellence Cognitive Interaction Technology (CITEC)（认知交互技术卓越中心）；Bielefeld University（比勒费尔德大学）
Adapt2Reward: Adapting Video-Language Models to Generalizable Robotic Rewards via Failure Prompts
School of Software Technology, Zhejiang University（浙江大学软件学院）；School of Computer Sciene and Technology, Hangzhou Dianzi University（杭州电子科技大学计算机学院）；China Mobile (Zhejiang) Research & Innovation Institute（中国移动（浙江）研究院）；State Key Lab of CAD&CG, Zhejiang University（浙江大学CAD&CG国家重点实验室）；The Hong Kong Polytechnic University（香港理工大学）；Zhiyuan Research Institute（智源研究院）；School of Computer Sciene and Technology, Xidian University（西安电子科技大学计算机学院）
Predictive Processing in Cognitive Robotics: a Review
Universidad Nacional Autónoma de México（墨西哥国立自治大学）；Scuola Superiore Sant’Anna（圣安娜高等研究院）；National Research Council（国家研究委员会）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Universidad Autónoma del Estado de Morelos（莫雷洛斯州自治大学）
Model Hierarchy Predictive Control of Robotic Systems
University of Notre Dame（圣母大学）
这封信提出了一种新的预测控制结构的高维机器人系统。相对于传统的模型预测控制（MPC）的方法来制定一个层次序列的优化问题的运动，所提出的工作制定了一个单一的优化问题提出了一个层次的模型，因此被命名为模型层次预测控制（MHPC）。MHPC被公式化为多阶段滚动时域轨迹优化（TO）问题，并且可以使用任何通用的多阶段TO求解器来实现。MHPC在四足动物、双足动物和四旋翼动物的模拟中进行了基准测试，证明了与全身MPC相当或超过全身MPC的控制性能，同时在每种情况下保持较低的计算成本。在MIT Mini Cheetah上进行了初步的间隙跳跃实验，离线生成的控制策略，证明了生成的轨迹的物理有效性，并激励在线MHPC在未来的工作中。
Beyond Successful Robot Demonstrations: Learning from Human Interactions and Modeling Failure Dynamics
Zhejiang University（浙江大学）；Beijing Zhongguancun Academy（北京中关村学院）；University of Science and Technology of China（中国科学技术大学）；Eastern Institute of Technology（东方理工学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Dual-Mode Synchronization Predictive Control of Robotic Manipulator
School of Mechanical and Electrical Engineering, Guangzhou University（广州大学机械与电气工程学院）；School of Automation, Guangdong University of Technology（广东工业大学自动化学院）
Diagnose, Correct, and Learn from Manipulation Failures via Visual Symbols
Beihang University（北京航空航天大学）；Shanghai Innovation Institute（上海创新研究院）；Southern University of Science and Technology（南方科技大学）；Shanghai Jiao Tong University（上海交通大学）
FailSafe: Reasoning and Recovery from Failures in Vision-Language-Action Models
Nanyang Technological University（南洋理工大学）；Centre for Frontier AI Research, A*STAR（A*STAR前沿人工智能研究中心）；Allen Institute for AI（艾伦人工智能研究所）；University of Washington（华盛顿大学）
