🤖 AI Summary
This work addresses the inefficiency and verbosity of object rearrangement on tabletops when limited to grasp-and-place actions by introducing non-grasping topple operations to enrich the action space. The authors formulate the task as a pebble-motion problem with aggregation operations through a directed graph abstraction, systematically incorporating topple—alongside other aggregate actions such as scooping—into the planning framework for stacked rearrangement for the first time. This abstraction generalizes naturally to additional aggregate manipulations beyond toppling. Experimental results in the IsaacSim simulation environment demonstrate that plans integrating topple actions significantly reduce execution time compared to pure grasp-and-place strategies, thereby validating the effectiveness and potential of enriched interactive action abstractions in manipulation tasks.
📝 Abstract
Efficient object manipulation strategies have significant impact in automation applications. In this work, the stack rearrangement in tabletop settings is studied, with a focus on augmenting the task planning domain with richer nonprehensile aggregating actions, in particular the toppling of objects from a stack to the table. Toppling can compress long sequences of intermediate relocations. Computed plans need to interleave pick-and-place actions with topple throughout its plan based on the problem. In order to generate the task plan and model an abstraction to compute solutions that include both pick-and-place and topple actions, a novel aggregating gadget for topple is introduced. Using this directed graphical abstraction, candidate task plan computation becomes a variant of the pebble motion problem, treating objects as pebbles. Benchmarks are then reported in a IsaacSim-based physics simulation. Results highlight clear benefits of achieving faster execution than solely using pick-and-place actions. Though this work primarily investigates the topple action, we demonstrate that similar abstractions can model other aggregating actions of interest, like scoop. The current work provides a preliminary, strong indication of the promising benefits of abstractions for rich object interactions in manipulation applications.