让机器人在关节故障时仍能完成任务，通过健康状态感知自适应控制。
Health-Conditioned Vision-Language-Action Models for Malfunction-Aware Robot Control
- 引入健康向量输入，使模型感知关节角度与扭矩状态
- 在128个遥操作数据上训练，实现不同故障配置下的成功任务执行
- 轻量级改造，适合需要容错能力的工业机器人应用
近年来，视觉-语言-动作（VLA）模型研究迅速发展。尽管部分工作关注任务失败的检测、预防与恢复，但通常未考虑机器人物理故障的适应问题。现实中，机器人常面临关节退化、执行器故障或夹爪失效等状况。本文提出一种故障感知（健康条件化）的VLA模型，将健康向量作为输入，包含关节运行角度与扭矩能力信息，使模型能根据当前健康状态调整预测以完成任务。为此，在VLA-Adapter架构中引入健康投影模块，并在我们收集的LIBERO环境上的故障机器人数据上进行训练，共采集128个遥操作的Libero-Spatial任务数据集。实验表明，仅通过轻量级改进，该模型即可在多种关节退化配置下成功执行任务，而原生预训练的VLA-Adapter Libero-Spatial-Pro模型则无法应对。代码与数据集即将开源于https://github.com/h-arslan/health-aware-vla。
原文摘要 · Abstract (English)
Research on Vision Language Action (VLA) models has been increasing rapidly in recent years. Although some of them focus on detecting, preventing, and recovering from task failures, they usually don't deal with adapting to robot's physical failures. In real-life scenarios, most robots face physical degradations in various ways such as joint degradation, actuator failure, or weak gripper. We introduce malfunction-aware (health-conditioned) VLA that takes a health vector as an input that gives information about robots' joints' operation angle and torque capability, and adapts its predictions to complete the tasks with the degraded joints. To achieve this, we inject a Health Projector module to the VLA-Adapter architecture and train it on malfunction robot data we collected on the LIBERO environment [1]. We collect 128 teleoperated episodes on Libero-Spatial tasks. Our results show that, with a very lightweight addition, the model can learn to operate successfully with different configurations of degraded joints which the default pretrained VLA-Adapter's Libero-Spatial-Pro model cannot. The code and dataset will be available soon at https://github.com/h-arslan/health-aware-vla
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