Recent research efforts have yielded significant advancements in manipulating objects under homogeneous settings where the robot is required to either manipulate rigid or deformable (soft) objects. However, the manipulation under heterogeneous setups that involve both deformable and rigid objects remains an unexplored area of research. Such setups are common in various scenarios that involve the transportation of heavy objects via ropes, e.g., on factory floors, at disaster sites, and in forestry. To address this challenge, we introduce DeRi-Bot, the first framework that enables the collaborative manipulation of rigid objects with deformable objects. Our framework comprises an Action Prediction Network (APN) and a Configuration Prediction Network (CPN) to model the complex pattern and stochasticity of soft-rigid body systems. We demonstrate the effectiveness of DeRi-Bot in moving rigid objects to a target position with ropes connected to robotic arms. Furthermore, DeRi-Bot is a distributive method that can accommodate an arbitrary number of robots or human partners without reconfiguration or retraining. We evaluate our framework in both simulated and real-world environments and show that it achieves promising results with strong generalization across different types of objects and multi-agent settings, including human-robot collaboration.

翻译：近期研究工作在同质化场景（机器人需操控刚体或可变形软体物体）中取得了显著进展，然而涉及可变形与刚体物体的异质化操控仍属研究空白。此类场景普遍存在于需通过绳索运输重物的多种情境中，例如工厂车间、灾害现场及林业作业。为应对这一挑战，我们提出DeRi-Bot——首个实现通过可变形物体协作操控刚体物体的框架。该框架包含动作预测网络（APN）与构型预测网络（CPN），用于建模软-刚体系统的复杂模式与随机性。我们通过在机械臂连接绳索的系统中将刚体物体移动至目标位置，验证了DeRi-Bot的有效性。此外，DeRi-Bot作为一种分布式方法，可在无需重新配置或重新训练的情况下适配任意数量的机器人或人类伙伴。我们在模拟环境与真实环境中评估该框架，实验表明其在多种物体类型与多智能体设置（包括人机协作）中均取得了优异性能，并展现出强大的泛化能力。