Moving On, Even When You're Broken: Fail-Active Trajectory Generation via Diffusion Policies Conditioned on Embodiment and Task
Dept. of Computer Science, University of Colorado Boulder, CO, USA（计算机科学系，科罗拉多大学博尔德分校）；Dept. of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, CO, USA（电气、计算机与能源工程系，科罗拉多大学博尔德分校）
Gilberto G. Briscoe-Martinez, Yaashia Gautam, Rahul Shetty, Anuj Pasricha, Marco M. Nicotra, Alessandro Roncone
备注：
To be published in the 2026 IEEE International Conference on Robotics & Automation
Robot failure is detrimental and disruptive, often requiring human intervention to recover. Our vision is 'fail-active' operation, allowing robots to safely complete their tasks even when damaged. Focusing on 'actuation failures', we introduce DEFT, a diffusion-based trajectory generator conditioned on the robot's current embodiment and task constraints. DEFT generalizes across failure types, supports constrained and unconstrained motions, and enables task completion under arbitrary failure. We evaluate DEFT in both simulation and real-world scenarios using a 7-DoF robotic arm. DEFT outperforms its baselines over thousands of failure conditions, achieving a 99.5% success rate for unconstrained motions versus RRT's 42.4%, and 46.4% for constrained motions versus differential IK's 30.9%. Furthermore, DEFT demonstrates robust zero-shot generalization by maintaining performance on failure conditions unseen during training. Finally, we perform real-world evaluations on two multi-step tasks, drawer manipulation and whiteboard erasing. These experiments demonstrate DEFT succeeding on tasks where classical methods fail. Our results show that DEFT achieves fail-active manipulation across arbitrary failure configurations and real-world deployments.
DiffMorphGrasp: A Diffusion Framework Conditioned on Hand Morphology for Cross-Embodiment Grasp Generation
Department of Engineering, King’s College London, Strand, London, WC2R 2LS, United Kingdom（伦敦国王学院工程系）；Imperial College London, London, SW7 2AZ, United Kingdom（伦敦帝国学院）
CrossTracer: Cross-Embodiment Navigation via VLA Model Reasoning and Trace Residuals Adapting
Peng Cheng Laboratory（鹏城实验室）；Southern University of Science and Technology（南方科技大学）；Innovation Investment Research Institute（创新投资研究院）；Soochow University（苏州大学）
Rapid Embodiment Adaptation for Quadrupedal Locomotion
UC San Diego（加州大学圣迭戈分校）；Stanford University（斯坦福大学）；TU Darmstadt（达姆施塔特工业大学）；Sudo AI GmbH（Sudo AI 有限公司）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Robotics Institute Germany（德国机器人研究所）
A4A: Cross-Embodiment Transfer of Action-Oriented 4D Affordances from Human Demonstrations
Shanghai Jiao Tong University（上海交通大学）；Rutgers University–New Brunswick（罗格斯大学新布朗斯维克分校）；Nanyang Technological University（南洋理工大学）；The Hong Kong University of Science and Technology (GZ)（香港科技大学（广州））；Shanghai AI Laboratory（上海人工智能实验室）
Multi-Embodiment Robotic Retargeting via Guided Diffusion Model
Southern University of Science and Technology（南方科技大学）；The Hong Kong University of Science and Technology（香港科技大学）；Zhejiang University-University of Illinois Urbana-Champaign Institute（浙江大学伊利诺伊大学厄巴纳-香槟校区联合学院）；LimX Dynamics
MachaGrasp: Morphology-Aware Cross-Embodiment Dexterous Hand Articulation Generation for Grasping
Robotics & Autonomous Systems Division, Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR-I 2 R)（机器人与自主系统 division，信息通信研究所，科技研究局）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）；Show Lab, National University of Singapore（Show Lab，国立新加坡大学）
Video-Language Critic: Transferable Reward Functions for Language-Conditioned Robotics
Department of Computer Science, Aalto University（艾尔沃斯大学计算机科学系）；Department of Computer Science, Toronto Metropolitan University（多伦多 Metropolitan 大学计算机科学系）；Department of Computer Science, University of Manchester（曼彻斯特大学计算机科学系）；Intel Corporation（英特尔公司）
A Cross-Environment and Cross-Embodiment Path Planning Framework via a Conditional Diffusion Model
Computer Engineering Department, University of Victoria（维多利亚大学计算机工程系）；Mila-Quebec AI Institute（魁北克AI研究院）；Mechanical Engineering Department, University of Victoria（维多利亚大学机械工程系）
AtomEgo: Exploring Ego-Robot Integration for Embodied Foundation Model Pretraining
INFIFORCE；Lionrock Artificial Intelligence Laboratory（狮岩人工智能实验室）；HKU（香港大学）；Tongji University（同济大学）；Huazhong University of Science and Technology（华中科技大学）
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）
T(R,O) Grasp: Efficient Graph Diffusion of Robot-Object Spatial Transformation for Cross-Embodiment Dexterous Grasping
Department of Computer Science, National University of Singapore（新加坡国立大学计算机科学系）；RoboScience；College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）
Learning Adaptive Cross-Embodiment Visuomotor Policy with Contrastive Prompt Orchestration
School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）；College of Automation Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学自动化工程学院）
BridgeV2W: Bridging Video Generation Models to Embodied World Models via Embodiment Masks
New Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences（模式识别新实验室（NLPR），自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）；Shandong University（山东大学）
AgniNav: Configuration-Driven Cross-Embodiment Local Planning for Robot Navigation
Eastern Institute of Technology, Ningbo, China（东方理工（宁波））；University of Nottingham, Nottingham, UK（诺丁汉大学）；University of Science and Technology of China, Hefei, China（中国科学技术大学）
Function beyond Form: Functional Correspondence for Cross-Embodiment Dexterous Grasp Generation
Southern University of Science and Technology（南方科技大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Shanghai Jiao Tong University（上海交通大学）；Rysen Robotics（睿森机器人）
