Robot failure is detrimental and disruptive, often requiring human intervention to recover. Maintaining safe operation under impairment to achieve task completion, i.e. fail-active operation, is our target. 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 evaluated DEFT in both simulation and real-world scenarios using a 7-DoF robotic arm. In simulation over thousands of joint-failure cases across multiple tasks, DEFT outperformed the baseline by up to 2 times. On failures unseen during training, it continued to outperform the baseline, indicating robust generalization in simulation. Further, we performed real-world evaluations on two multi-step tasks, drawer manipulation and whiteboard erasing. These experiments demonstrated DEFT succeeding on tasks where classical methods failed. 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。我们的目标是在受损情况下维持安全运行以完成任务，即实现失效主动操作。针对驱动故障，我们提出了DEFT，一种基于扩散的轨迹生成器，它以机器人当前体态和任务约束为条件。DEFT能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。我们使用一个7自由度机械臂，在仿真和真实场景中评估了DEFT。在涵盖多个任务的数千个关节故障案例的仿真中，DEFT的性能比基线方法高出最多2倍。对于训练中未见过的故障，它仍持续优于基线，表明其在仿真中具有鲁棒的泛化能力。此外，我们在两个多步骤任务（抽屉操作和白板擦除）上进行了真实世界评估。这些实验证明，DEFT在经典方法失败的任务中取得了成功。我们的结果表明，DEFT能在任意故障配置和真实世界部署中实现失效主动操作。