Extrinsic manipulation, the use of environment contacts to achieve manipulation objectives, enables strategies that are otherwise impossible with a parallel jaw gripper. However, orchestrating a long-horizon sequence of contact interactions between the robot, object, and environment is notoriously challenging due to the scene diversity, large action space, and difficult contact dynamics. We observe that most extrinsic manipulation are combinations of short-horizon primitives, each of which depend strongly on initializing from a desirable contact configuration to succeed. Therefore, we propose to generalize one extrinsic manipulation trajectory to diverse objects and environments by retargeting contact requirements. We prepare a single library of robust short-horizon, goal-conditioned primitive policies, and design a framework to compose state constraints stemming from contacts specifications of each primitive. Given a test scene and a single demo prescribing the primitive sequence, our method enforces the state constraints on the test scene and find intermediate goal states using inverse kinematics. The goals are then tracked by the primitive policies. Using a 7+1 DoF robotic arm-gripper system, we achieved an overall success rate of 80.5% on hardware over 4 long-horizon extrinsic manipulation tasks, each with up to 4 primitives. Our experiments cover 10 objects and 6 environment configurations. We further show empirically that our method admits a wide range of demonstrations, and that contact retargeting is indeed the key to successfully combining primitives for long-horizon extrinsic manipulation. Code and additional details are available at stanford-tml.github.io/extrinsic-manipulation.

翻译：外源性操控利用环境接触实现操控目标，使平行爪夹持器无法完成的策略成为可能。然而，由于场景多样性、大动作空间以及复杂的接触动力学，协调机器人、物体与环境之间长时程的接触交互序列极具挑战性。我们观察到，大多数外源性操控是短时程基元的组合，每个基元的成功高度依赖于从期望的接触构型初始化。因此，我们提出通过重定向接触需求，将单条外源性操控轨迹泛化到不同物体和环境中。我们构建了一个鲁棒短时程、目标条件化基元策略的单一库，并设计了一个框架来组合每个基元接触规范所产生的状态约束。给定测试场景和指定基元序列的单一演示，我们的方法对测试场景施加状态约束，并使用逆运动学找到中间目标状态。随后，这些目标由基元策略跟踪。使用7+1自由度机械臂-夹持器系统，我们在4个长时程外源性操控任务（每个任务最多包含4个基元）的硬件实验中实现了80.5%的总体成功率。实验覆盖10种物体和6种环境配置。我们进一步通过实验证明，该方法能够适应广泛范围的演示，并且接触重定向确实是成功组合基元以实现长时程外源性操控的关键。代码和更多细节见stanford-tml.github.io/extrinsic-manipulation。