Deformable object manipulation stands as one of the most captivating yet
formidable challenges in robotics. While previous techniques have predominantly
relied on learning latent dynamics through demonstrations, typically
represented as either particles or images, there exists a pertinent limitation:
acquiring suitable demonstrations, especially for long-horizon tasks, can be
elusive. Moreover, basing learning entirely on demonstrations can hamper the
model's ability to generalize beyond the demonstrated tasks. In this work, we
introduce a demonstration-free hierarchical planning approach capable of
tackling intricate long-horizon tasks without necessitating any training. We
employ large language models (LLMs) to articulate a high-level, stage-by-stage
plan corresponding to a specified task. For every individual stage, the LLM
provides both the tool's name and the Python code to craft intermediate subgoal
point clouds. With the tool and subgoal for a particular stage at our disposal,
we present a granular closed-loop model predictive control strategy. This
leverages Differentiable Physics with Point-to-Point correspondence
(DiffPhysics-P2P) loss in the earth mover distance (EMD) space, applied
iteratively. Experimental findings affirm that our technique surpasses multiple
benchmarks in dough manipulation, spanning both short and long horizons.
Remarkably, our model demonstrates robust generalization capabilities to novel
and previously unencountered complex tasks without any preliminary
demonstrations. We further substantiate our approach with experimental trials
on real-world robotic platforms.

这项研究介绍了一种无需演示的层次规划方法，能够处理复杂的长期任务，并展示了在面对新颖和以前未遇到的复杂任务时，模型具备强大的泛化能力。