3. Related Work

3.0.1. Introduction

In this chapter we’ll cover literature that relates to this dissertation on reusable action structure, reactive task coordination, human-machine coordination, perception, and real robot door task demonstrations. Architectural references include CLARAty, Affordance Templates, Affordance Primitives, Coactive Design, Director, FlexBE, RAFCON, Drawing Board, and the CENTAURO behavior tree systems. Door task references span from classical and model-based mobile-manipulator and humanoid systems to recent learned legged and humanoid policies. The systems with metrics that overlap our benchmark are compared with each other and this thesis in Evaluation.

Figure 3.1 maps every system reviewed in this chapter across four dimensions: execution boundary, runtime editability, perception model, and door evidence. The execution boundary refers to where the process that controls the robot runs. It can be off-board, operator-in-the-loop, or purely on-board/robot-local. This is one part of qualifying our “Independence from External Systems” characteristic in Desirable Characteristics.

In Figure 3.1, each row is one system. The filled cells place that row on the execution-boundary axis (left band) and the runtime-editability axis (right band). The two rightmost columns encode the perception model used at task time, distinguishing behavior-time authored perception from a fixed onboard pipeline, learned end-to-end perception, and reliance on external measurement, alongside the strength of the system’s door-task evidence on a four-level scale from none to repeated-trial real-world. This thesis is the only row that combines robot-local execution, runtime structural and perception edits, behavior-time authored perception, and repeated-trial real-world door evidence. DoorMan is the only off-board entry, and the learned door systems concentrate on the policy-retrain column rather than runtime structural edits.