RISE：通过随机编码实现鲁棒模仿
RISE: Robust Imitation through Stochastic Encoding
AI总结：
针对行为克隆在真实动态环境中易受观测干扰的问题，本文提出 RISE 框架，利用变分潜在空间编码环境参数，提升离线模仿策略对噪声和不确定性的安全鲁棒性。
摘要：
确保机器人系统的安全仍是一项根本性挑战，尤其是在动态环境中部署模仿学习等离线策略学习方法时。传统行为克隆（BC）在未经微调直接部署时往往无法泛化，因为它没有考虑真实、变化环境中出现的观测干扰。为解决这一局限，我们提出 RISE（Robust Imitation through Stochastic Encodings，通过随机编码实现鲁棒模仿），这是一种新颖的模仿学习框架，通过变分潜在表征，在策略学习中显式处理环境参数的错误测量。该框架将障碍物状态、朝向和速度等参数编码到平滑的变分潜在空间中，以提升测试时的泛化能力。这使离线训练的策略能够生成对感知噪声和环境不确定性更鲁棒的动作。我们在两个机器人平台上验证了该方法，即自主地面车辆和 Franka Emika Panda 机械臂；结果表明，与基线方法相比，本方法在保持到达目标性能的同时，提升了安全鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
