Hybrid cognitive robotics+ & explorations therein with the robot peri.2
Loading...
Authors
Slowik, John, Kenneth
Issue Date
2025-07
Type
Electronic thesis
Thesis
Thesis
Language
en_US
Keywords
Computer science
Alternative Title
Abstract
In \textit{cognitive robotics}, all substantive actions on the part ofa robot, both physical and mental, are a function of reasoning over
the robot's beliefs directed at declarative
propositions, where the propositions are represented as formulae in the formal
structure of some logical system, and the reasoning is precise deduction defined
in the logic's \textit{proof theory}. Joined by colleagues, I have
expanded via three steps the discipline of cognitive robotics as
defined by Levesque by allowing: (i)
cognitive attitudes beyond belief to be included
(e.g.,~\textit{knowing}, \textit{intending}, \textit{desiring}), as
long as these attitudes are directed toward content expressed in
formulae in the relevant logic (as in the case of
\textit{belief}); (ii) reasoning to be non-deductive; and (iii) the
content in formulae to be non-declarative (e.g., importantly,
imperative). My work in this expanded approach to cognitive robotics
has already met with considerable success, as shown by the initial
prowess of Perception Enabled Robotic Intelligence 2, a cognitive
robot known simply as `PERI.2'. However, it is essential to note that robotics and AI have recentlyreceived much attention because of advances not in logic-based
techniques, but because of the success of deep- and
reinforcement-learning techniques and their application to big data.
This provided adequate reason to question a strictly logicist approach
in cognitive robotics. In response, I adopt and find encouraging
results in a hybrid approach to cognitive
robotics in which I marry the logicist approach of (i)--(iii) with
approaches like deep learning; thus, \textit{Hybrid Cognitive Robotics Plus} (HCR$^+$).
Deep learning, as is well-known, eschews manual engineering based on
reasoning over structured, declarative data; HCR$^+$ combines such
engineered and logicist techniques with sub-symbolic
and data-driven techniques (e.g.\ Machine Learning). The fact is,
while sub-symbolic reasoning is powerful in some domains
and for some problems, there are numerous applications where it remains
unacceptable due to a desire for procedures requiring precise reasoning and
programming, and solutions that rely on
such reasoning and programming:\ e.g.,~safety, reliability, and formal
verification. On the other hand, a swathe of problems are simply
beyond the reach of logicist techniques, for instance robust image
recognition. The research approach that drives this dissertation is
the development and use of hybrid techniques that integrate logic-based
algorithms with sub-symbolic processing. Using these techniques to increase the capability of PERI.2 will bethe main thrust of the dissertation. My extension of the foundation
erected in cognitive robotics in working with colleagues will include
building out deeper theory, achieving markedly better implementations
in prior application areas, and engineering implementations in some
new application areas. This better implementation is the application of deep logical reasoning with perception to object manipulation to solve complex
problems; use of a physical robotic manipulator through the
development and deployment of PERI.2 expanding on prior works featuring the incomplete robot. Overall, the robot's use of perception with logic remains unique among its peers. Previously un-attempted application areas are solved through this technique; the key specific challenge is solving physical logic puzzles. A social deduction challenge was also solved. Additional challenges were unmet but substantiate the work, including sculpting based on literary prompts, working in occluded perception environments, and tasks in in-the-field
emergency medicine (e.g.,~applying splints for bone fractures using
malleable material). Each of these problems involves deep logical
reasoning to reach a solution that is reliable, explainable, and safe
in a real-world environment. Each problem benefits greatly from
sub-symbolic processing due to the need to interpret complex visual
scenes, a traditionally challenging task for logic-based approaches.
While each domain into which my specific challenges fall has been
worked on for a long while, my work pioneers the integration of
cognitive robotics, modern machine learning, and classical engineering
robotics and human-robot interaction. Work aimed at human-level robot
capability currently being carried out by industrial ``heavyweights''
like Google, OpenAI, Boston Dynamics, and Intuitiv is nearly
exclusively sub-symbolic in nature and would, I believe, benefit
greatly from my hybrid approach. Lastly, a significant part of the novelty of my research inheres inthe use of state-of-the-art computational logic; I shall specifically
make use of, and contribute to the refinement of, automated reasoners
and planning systems (in the latter case, the RAIR Lab's Spectra
planner).
Description
July2025
School of Science
School of Science
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY