FC$^3$: Feasibility-Based Control Chain Coordination
Jason Harris, Danny Driess, Marc Toussaint

TL;DR
FC$^3$ is a framework for hierarchical controller coordination that uses geometric feasibility reasoning to improve robustness and adaptability in complex tasks.
Contribution
It introduces a novel approach that leverages geometric features and constraints for controller chain coordination and task feasibility assessment.
Findings
Demonstrates robustness in real-world experiments.
Shows effective task feasibility evaluation and controller switching.
Achieves reliable behavior under interference.
Abstract
Hierarchical coordination of controllers often uses symbolic state representations that fully abstract their underlying low-level controllers, treating them as "black boxes" to the symbolic action abstraction. This paper proposes a framework to realize robust behavior, which we call Feasibility-based Control Chain Coordination (FC). Our controllers expose the geometric features and constraints they operate on. Based on this, FC can reason over the controllers' feasibility and their sequence feasibility. For a given task, FC first automatically constructs a library of potential controller chains using a symbolic action tree, which is then used to coordinate controllers in a chain, evaluate task feasibility, as well as switching between controller chains if necessary. In several real-world experiments we demonstrate FC's robustness and awareness of the task's feasibility…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsFormal Methods in Verification · Reinforcement Learning in Robotics · Intelligent Tutoring Systems and Adaptive Learning
