让机器人在干扰中更安全，通过随机编码提升模仿学习鲁棒性
RISE: Robust Imitation through Stochastic Encoding
- 用变分潜空间编码环境参数，显式建模观测误差
- 在两种机器人上测试，显著提升抗干扰能力且不损失任务完成率
- 适合部署于动态真实场景的机器人系统，尤其关注安全性
确保机器人系统安全仍是重大挑战，尤其在动态环境中部署离线策略学习方法（如模仿学习）时。传统行为克隆（BC）在未微调的情况下难以泛化，因其未考虑现实变化环境中观测值的扰动。为此，我们提出 RISE（Robust Imitation through Stochastic Encodings），一种新型模仿学习框架，通过变分潜表示显式处理环境参数的错误测量。该框架将障碍物状态、朝向、速度等参数编码至平滑的变分潜空间，提升测试时的泛化能力，使离线训练的策略对感知噪声和环境不确定性更具鲁棒性。我们在两台机器人平台上验证：自主地面车辆与 Franka Emika Panda 机械臂，结果表明相比基线方法，本方法在保持目标达成性能的同时显著提升了安全鲁棒性。
原文摘要 · Abstract (English)
Ensuring safety in robotic systems remains a fundamental challenge, especially when deploying offline policy-learning methods such as imitation learning in dynamic environments. Traditional behavior cloning (BC) often fails to generalize when deployed without fine-tuning as it does not account for disturbances in observations that arises in real-world, changing environments. To address this limitation, we propose RISE (Robust Imitation through Stochastic Encodings), a novel imitation-learning framework that explicitly addresses erroneous measurements of environment parameters into policy learning via a variational latent representation. Our framework encodes parameters such as obstacle state, orientation, and velocity into a smooth variational latent space to improve test time generalization. This enables an offline-trained policy to produce actions that are more robust to perceptual noise and environment uncertainty. We validate our approach on two robotic platforms, an autonomous ground vehicle and a Franka Emika Panda manipulator and demonstrate improved safety robustness while maintaining goal-reaching performance compared to baseline methods.
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