Approximately Optimal Global Planning for Contact-Rich SE(2) Manipulation on a Graph of Reachable Sets

2026-01-15

AI Summary

Existing model-based planning methods for contact-rich manipulation often prioritize feasibility over global optimality, thereby underutilizing the full potential of surface contact between the manipulator and the object. This work proposes a two-stage planning paradigm: offline, a graph structure is constructed based on SE(2) reachable sets, where each node encodes all achievable orientations for a specific grasp and object pose; online, global optimization is performed via graph search, integrated with local motion planning to generate efficient sequential manipulation actions. To the best of our knowledge, this is the first approach to achieve near-globally optimal planning for contact-rich manipulation tasks. In benchmark scenarios, it reduces task cost by 61%, achieves a 91% success rate over 250 queries, and completes each planning instance in under one minute.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Manipulation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

If we consider human manipulation, it is clear that contact-rich manipulation (CRM)-the ability to use any surface of the manipulator to make contact with objects-can be far more efficient and natural than relying solely on end-effectors (i.e., fingertips). However, state-of-the-art model-based planners for CRM are still focused on feasibility rather than optimality, limiting their ability to fully exploit CRM's advantages. We introduce a new paradigm that computes approximately optimal manipulator plans. This approach has two phases. Offline, we construct a graph of mutual reachable sets, where each set contains all object orientations reachable from a starting object orientation and grasp. Online, we plan over this graph, effectively computing and sequencing local plans for globally optimized motion. On a challenging, representative contact-rich task, our approach outperforms a leading planner, reducing task cost by 61%. It also achieves a 91% success rate across 250 queries and maintains sub-minute query times, ultimately demonstrating that globally optimized contact-rich manipulation is now practical for real-world tasks.

Problem

• contact-rich manipulation
• global planning
• optimality
• reachable sets
• SE(2) manipulation

Innovation

• contact-rich manipulation
• reachable sets
• global planning
• approximate optimality
• SE(2) manipulation