A Behavior Architecture for Fast Humanoid Robot Door Traversals
Duncan Calvert, Luigi Penco, Dexton Anderson, Tomasz Bialek, Arghya, Chatterjee, Bhavyansh Mishra, Geoffrey Clark, Sylvain Bertrand, Robert, Griffin

TL;DR
This paper introduces a comprehensive architecture enabling humanoid robots to efficiently perceive, plan, and execute door traversals across various types, integrating GPU-accelerated perception, behavior trees, and whole-body manipulation for rapid, reusable actions.
Contribution
It presents a novel, layered behavior architecture combining perception, planning, and control for fast, adaptable door traversal in humanoid robots, supporting rapid behavior authoring and reusability.
Findings
Successful real-world door traversal demonstrations
Effective perception combining neural networks and classical vision
Rapid behavior authoring for unseen door types
Abstract
Towards the role of humanoid robots as squad mates in urban operations and other domains, we identified doors as a major area lacking capability development. In this paper, we focus on the ability of humanoid robots to navigate and deal with doors. Human-sized doors are ubiquitous in many environment domains and the humanoid form factor is uniquely suited to operate and traverse them. We present an architecture which incorporates GPU accelerated perception and a tree based interactive behavior coordination system with a whole body motion and walking controller. Our system is capable of performing door traversals on a variety of door types. It supports rapid authoring of behaviors for unseen door types and techniques to achieve re-usability of those authored behaviors. The behaviors are modelled using trees and feature logical reactivity and action sequences that can be executed with…
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Taxonomy
TopicsAdvanced Malware Detection Techniques · Robotic Locomotion and Control · Autonomous Vehicle Technology and Safety
