Robotic manipulation of deformable and fragile objects presents significant challenges, as excessive stress can lead to irreversible damage to the object. While existing solutions rely on accurate object models or specialized sensors and grippers, this adds complexity and often lacks generalization. To address this problem, we present a vision-based reinforcement learning approach that incorporates a stress-penalized reward to discourage damage to the object explicitly. In addition, to bootstrap learning, we incorporate offline demonstrations as well as a designed curriculum progressing from rigid proxies to deformables. We evaluate the proposed method in both simulated and real-world scenarios, showing that the policy learned in simulation can be transferred to the real world in a zero-shot manner, performing tasks such as picking up and pushing tofu. Our results show that the learned policies exhibit a damage-aware, gentle manipulation behavior, demonstrating their effectiveness by decreasing the stress applied to fragile objects by 36.5% while achieving the task goals, compared to vanilla RL policies.

翻译：机器人对柔性与易碎物体的操控面临重大挑战，因为过大的应力可能导致物体不可逆的损伤。现有解决方案依赖于精确的物体模型或专用传感器与夹爪，这增加了系统复杂性且往往缺乏泛化能力。为解决这一问题，我们提出一种基于视觉的强化学习方法，通过引入应力惩罚奖励机制来显式抑制对物体的损伤。此外，为加速学习进程，我们整合了离线示范数据以及从刚性代理物体到柔性物体的渐进式课程设计。我们在仿真和现实场景中评估了所提方法，结果表明在仿真中学习的策略能够以零样本方式迁移至现实世界，成功完成如抓取和推动豆腐等任务。与原始强化学习策略相比，学习到的策略展现出损伤感知的温和操控行为，在实现任务目标的同时将施加于易碎物体的应力降低了36.5%，证明了其有效性。