通过接触重定向实现长时序外部操作的单次迁移
One-Shot Transfer of Long-Horizon Extrinsic Manipulation Through Contact Retargeting
AI总结：
针对长时序外部操作规划难的问题，提出基于接触重定向的单次迁移方法，通过组合短时序基元的接触约束、结合逆运动学求解目标状态，在硬件实验中取得80.5%的成功率。
摘要：
外部操作（extrinsic manipulation）指利用环境接触达成操作目标的技术，可实现平行夹爪无法完成的操作策略。然而，由于场景多样性、动作空间大以及接触动力学复杂，规划机器人、物体与环境之间的长时序接触交互序列极具挑战性。我们观察到，大多数外部操作由短时序基元组合而成，每个基元的成功都高度依赖于从理想的接触构型初始化。因此，我们提出通过重定向接触需求，将单条外部操作轨迹泛化到不同的物体和环境中。我们构建了一个包含鲁棒短时序、目标条件基元策略的单一库，并设计了一个框架来组合由每个基元的接触规范衍生的状态约束。给定测试场景和一条指定基元序列的单次演示，我们的方法在测试场景上施加状态约束，并通过逆运动学求解中间目标状态，随后由基元策略跟踪这些目标。在7+1自由度的机械臂-夹爪系统上，我们在4项长时序外部操作任务的硬件实验中取得了80.5%的总体成功率，每项任务最多包含4个基元。实验覆盖了10个物体和6种环境配置。我们进一步通过实验证明，该方法可适配多种演示，且接触重定向确实是成功组合基元以完成长时序外部操作的关键。代码及更多细节可访问stanford-tml.github.io/extrinsic-manipulation。
英文摘要：
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.
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。
