外观
论文arXiv 2609.216901 个月前
RAYA：学习在何处及何时干预以实现机器人恢复
原文链接：RAYA: Learning Where and When to Intervene for Robot Recovery（arXiv 2609.21690 · 发布日期：2026-09-18）
RAYA: Learning Where and When to Intervene for Robot Recovery
RAYA将学习的有限时域可恢复性裕度和权重调度嵌入最优控制器，并在四旋翼与自动车仿真及Crazyflie真机上验证。
核心要点
- 仅预测故障仍可能无法避免失败，事后安全否决和固定任务优先级会阻碍恢复。
- RAYA在带硬约束的最优控制器内加入学习的可恢复性裕度，并用有界学习调度器调整任务权重。
- 在四旋翼和自动车基准中，每个控制器7200次仿真，RAYA提升存活率并零样本迁移到多种未见情况。
- 嵌入式RAYA部署在35g Crazyflie上；项目网站为https://raya-control.github.io/。
验证范围与局限：验证范围包括四旋翼与自动车仿真基准，以及风中存在气动失配或未建模40%电机指令损失的Crazyflie真机飞行。
英文摘要 (Abstract)
A robot can predict failure and still be unable to prevent it. By the time a safety mechanism reacts, the nominal plan may already have spent the control authority that recovery requires, and fixed task priorities may block whatever response remains. Our key insight is that both aspects are decided inside the controller. Recoverability must inform actions while they are chosen rather than veto them afterward, and task objectives must be adapted as recoverability shrinks. Building on this, we present RAYA, a hybrid learned-analytic framework that places a learned finite-horizon recoverability margin inside an optimal controller with hard constraints and pairs it with a bounded learned scheduler that shifts task weights to facilitate recovery. Across 7,200 simulation episodes per controller spanning quadrotor and autonomous-vehicle benchmarks, RAYA not only improves survival rates, but also transfers the learned components zero-shot to unseen trajectories, disturbances, plant shifts, and friction layouts. We developed an embedded realization of RAYA and deployed it on-board a 35g Crazyflie quadrotor. Across 40 combined hardware flights under wind with either aerodynamic mismatch or an unmodeled 40% motor-command loss, each of three baselines fails in all trials, while RAYA completes 10/10 six-cycle missions. Project Website: https://raya-control.github.io/.
作者：Ishaan Mahajan, Charles Chen, Frederike Dümbgen, Brian Plancher
来源
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