用物体姿态轨迹建模任务，让机器人学会模仿人类动作。
SPOT: SE(3) Pose Trajectory Diffusion for Object-Centric Manipulation
- 以物体相对目标的SE(3)姿态轨迹为对象中心表示
- 仅需8个手机拍摄演示即完成真实世界所有任务
- 支持无动作标注的人类手势示范和跨机器人泛化
我们提出SPOT，一种以物体为中心的模仿学习框架。核心思想是通过物体相对于目标的SE(3)姿态轨迹来表征每个任务。该方法将具身动作与感官输入解耦，可从多种演示类型中学习，包括基于动作和无动作的人类手部示范，以及跨具身泛化。此外，物体姿态轨迹天然捕捉了来自示范的规划约束，无需手动设计规则。为指导机器人执行任务，使用姿态轨迹条件化扩散策略。我们在仿真和真实世界任务中系统评估该方法。在真实世界评估中，仅使用8个用iPhone拍摄的示范，我们的方法成功完成所有任务并完全遵守任务约束。
原文摘要 · Abstract (English)
We introduce SPOT, an object-centric imitation learning framework. The key idea is to capture each task by an object-centric representation, specifically the SE(3) object pose trajectory relative to the target. This approach decouples embodiment actions from sensory inputs, facilitating learning from various demonstration types, including both action-based and action-less human hand demonstrations, as well as cross-embodiment generalization. Additionally, object pose trajectories inherently capture planning constraints from demonstrations without the need for manually-crafted rules. To guide the robot in executing the task, the object trajectory is used to condition a diffusion policy. We systematically evaluate our method on simulation and real-world tasks. In real-world evaluation, using only eight demonstrations shot on an iPhone, our approach completed all tasks while fully complying with task constraints. Project page: https://nvlabs.github.io/object_centric_diffusion
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