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.

翻译：机器人故障具有危害性和破坏性，通常需要人工干预才能恢复。我们提出“故障主动”操作愿景，使机器人即使在受损状态下也能安全完成任务。针对“执行器故障”，我们提出了DEFT——一种基于扩散的轨迹生成器，其生成过程以机器人的当前体态和任务约束为条件。DEFT能够泛化至多种故障类型，支持约束与非约束运动，并能在任意故障下实现任务完成。我们在仿真和真实场景中使用7自由度机械臂对DEFT进行评估。DEFT在数千种故障条件下均优于基线方法：在非约束运动中达到99.5%的成功率（RRT为42.4%），在约束运动中达到46.4%的成功率（微分逆运动学为30.9%）。此外，DEFT展现出鲁棒的零样本泛化能力，在训练中未见的故障条件下仍能保持性能。最后，我们在抽屉操作和白板擦除两个多步骤任务上进行了真实场景评估。实验表明，DEFT在传统方法失效的任务中仍能成功执行。我们的研究结果证明，DEFT能够在任意故障配置和实际部署中实现故障主动操作。