Planning for quasi-static manipulation tasks via an intrinsic haptic metric

2024-11-07

arXiv.org

AI Summary

This work addresses the planning challenge of robotic insertion in highly cluttered environments (e.g., densely packed bookshelves), where strategic contact—pushing neighboring objects—is required to create insertion space. Conventional planners fail due to their strict contact avoidance, neglect of force interactions, and inability to model indirect manipulability. To overcome this, the authors formulate quasi-static insertion as motion planning on an implicit equilibrium manifold. They introduce a haptics-aware cost function grounded in an intrinsic Riemannian metric, replacing hand-crafted heuristics. Further, they propose an adaptive graph-search algorithm that jointly optimizes robot configuration, object poses, contact points, and haptic distances, leveraging superellipsoidal geometry and a differentiable simplified contact model. In simulation, the method autonomously discovers human-like wedging strategies, generalizes to rigid-body manipulation, and exhibits robustness under variations in object stiffness and initial pose.

Technology Category

Intelligent Robots: ManipulationHumans and AI: Human-Aware Planning and Behavior PredictionPlanning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsResponsible Web: Human-perceived consequences of algorithmic deployment on the webSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Contact-rich manipulation often requires strategic interactions with objects, such as pushing to accomplish specific tasks. We propose a novel scenario where a robot inserts a book into a crowded shelf by pushing aside neighboring books to create space before slotting the new book into place. Classical planning algorithms fail in this context due to limited space and their tendency to avoid contact. Additionally, they do not handle indirectly manipulable objects or consider force interactions. Our key contributions are: i) re-framing quasi-static manipulation as a planning problem on an implicit manifold derived from equilibrium conditions; ii) utilizing an intrinsic haptic metric instead of ad-hoc cost functions; and iii) proposing an adaptive algorithm that simultaneously updates robot states, object positions, contact points, and haptic distances. We evaluate our method on such crowded bookshelf insertion task but it is a general formulation to rigid bodies manipulation tasks. We propose proxies to capture contact point and force, with superellipse to represent objects. This simplified model guarantee the differentiablity. Our framework autonomously discovers strategic wedging-in policies while our simplified contact model achieves behavior similar to real world scenarios. We also vary the stiffness and initial positions to analysis our framework comprehensively. The video can be found at https://youtu.be/eab8umZ3AQ0.

Problem

• Planning for contact-rich quasi-static manipulation tasks.
• Handling indirectly manipulable objects with force interactions.
• Developing adaptive algorithms for robot and object states.

Innovation

• Planning on implicit manifold derived from equilibrium.
• Using intrinsic haptic metric for manipulation tasks.
• Adaptive algorithm updating states and contact points.