用少量交互数据快速优化物理模型，让机器人更灵活地推动物体。
Incremental Few-Shot Adaptation for Non-Prehensile Object Manipulation using Parallelizable Physics Simulators
- 基于可并行的刚体物理仿真，通过采样优化参数来适应新物体。
- 仅需5次交互示例，即可在模拟和真实机器人上实现稳定推物控制。
- 适合需要快速适应新任务的现实场景机器人应用。
少样本适应是智能机器人在开放世界（如日常环境或柔性产线）中执行任务的重要能力。本文提出一种新型非抓取操作方法，可增量式地为基于物理的动力学模型进行少样本适配，用于模型预测控制（MPC）。模型预测通过少量由MPC收集的机器人-物体交互示例进行对齐，利用可并行的刚体物理仿真作为动态世界模型，并采用基于采样的优化方法调整模型参数。优化后的动力学模型可进一步用于高效的采样式优化MPC。我们在模拟环境和真实机器人上进行了物体推动实验，验证了该方法的有效性。
原文摘要 · Abstract (English)
Few-shot adaptation is an important capability for intelligent robots that perform tasks in open-world settings such as everyday environments or flexible production. In this paper, we propose a novel approach for non-prehensile manipulation which incrementally adapts a physics-based dynamics model for model-predictive control (MPC). The model prediction is aligned with a few examples of robot-object interactions collected with the MPC. This is achieved by using a parallelizable rigid-body physics simulation as dynamic world model and sampling-based optimization of the model parameters. In turn, the optimized dynamics model can be used for MPC using efficient sampling-based optimization. We evaluate our few-shot adaptation approach in object pushing experiments in simulation and with a real robot.
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