RAYA: Learning Where and When to Intervene for Robot Recovery

2026-09-18

AI Summary

为解决机器人在预测失败后仍无法防止的问题，提出RAYA框架，结合学习与分析方法，在控制器中引入可恢复性边界和任务权重调度，提高生存率。

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/.

Problem

• robot recovery
• controller
• recoverability
• task objectives
• failure prediction

Innovation

• Recoverability Margin
• Optimal Controller
• Learned Scheduler
• Task Adaptation
• Hybrid Framework