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1. Knowledge Authoring for Rules and Actions

   https://doi.org/10.1017/S1471068423000169  

   WANG, YUHENG; FODOR, PAUL; KIFER, MICHAEL (July 2023, Theory and Practice of Logic Programming)

   Abstract Knowledge representation and reasoning (KRR) systems describe and reason with complex concepts and relations in the form of facts and rules. Unfortunately, wide deployment of KRR systems runs into the problem that domain experts have great difficulty constructing correct logical representations of their domain knowledge. Knowledge engineers can help with this construction process, but there is a deficit of such specialists. The earlier Knowledge Authoring Logic Machine (KALM) based on Controlled Natural Language (CNL) was shown to have very high accuracy for authoring facts and questions. More recently, KALM^FL, a successor of KALM, replaced CNL withfactualEnglish, which is much less restrictive and requires very little training from users. However, KALM^FLhas limitations in representing certain types of knowledge, such as authoring rules for multi-step reasoning or understanding actions with timestamps. To address these limitations, we propose KALM^RAto enable authoring of rules and actions. Our evaluation using the UTI guidelines benchmark shows that KALM^RAachieves a high level of correctness (100%) on rule authoring. When used for authoring and reasoning with actions, KALM^RAachieves more than 99.3% correctness on the bAbI benchmark, demonstrating its effectiveness in more sophisticated KRR jobs. Finally, we illustrate the logical reasoning capabilities of KALM^RAby drawing attention to the problems faced by the recently made famous AI, ChatGPT. 

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2. Knowledge Authoring with Factual English

   https://doi.org/10.4204/EPTCS.364.10  

   Wang, Yuheng; Borca-Tasciuc, Giorgian; Goel, Nikhil; Fodor, Paul; Kifer, Michael (August 2022, Electronic proceedings in theoretical computer science)

   Lierler, Yuliya; Morales, Jose F; Dodaro, Carmine; Dahl, Veroniica; Gebser, Martin; Tekle, Tuncay (Ed.)

   Knowledge representation and reasoning (KRR) systems represent knowledge as collections of facts and rules. Like databases, KRR systems contain information about domains of human activities like industrial enterprises, science, and business. KRRs can represent complex concepts and relations, and they can query and manipulate information in sophisticated ways. Unfortunately, the KRR technology has been hindered by the fact that specifying the requisite knowledge requires skills that most domain experts do not have, and professional knowledge engineers are hard to find. One solution could be to extract knowledge from English text, and a number of works have attempted to do so (OpenSesame, Google's Sling, etc.). Unfortunately, at present, extraction of logical facts from unrestricted natural language is still too inaccurate to be used for reasoning, while restricting the grammar of the language (so-called controlled natural language, or CNL) is hard for the users to learn and use. Nevertheless, some recent CNL-based approaches, such as the Knowledge Authoring Logic Machine (KALM), have shown to have very high accuracy compared to others, and a natural question is to what extent the CNL restrictions can be lifted. In this paper, we address this issue by transplanting the KALM framework to a neural natural language parser, mStanza. Here we limit our attention to authoring facts and queries and therefore our focus is what we call factual English statements. Authoring other types of knowledge, such as rules, will be considered in our followup work. As it turns out, neural network based parsers have problems of their own and the mistakes they make range from part-of-speech tagging to lemmatization to dependency errors. We present a number of techniques for combating these problems and test the new system, KALMFL (i.e., KALM for factual language), on a number of benchmarks, which show KALMFL achieves correctness in excess of 95%. 

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3. Design Human-Robot Collaborative Lifting Task Using Optimization

   https://doi.org/10.1115/DETC2021-71818  

   Arefeen, Asif; Xiang, Yujiang (August 2021, ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Abstract In this paper, an optimization-based dynamic modeling method is used for human-robot lifting motion prediction. The three-dimensional (3D) human arm model has 13 degrees of freedom (DOFs) and the 3D robotic arm (Sawyer robotic arm) has 10 DOFs. The human arm and robotic arm are built in Denavit-Hartenberg (DH) representation. In addition, the 3D box is modeled as a floating-base rigid body with 6 global DOFs. The interactions between human arm and box, and robot and box are modeled as a set of grasping forces which are treated as unknowns (design variables) in the optimization formulation. The inverse dynamic optimization is used to simulate the lifting motion where the summation of joint torque squares of human arm is minimized subjected to physical and task constraints. The design variables are control points of cubic B-splines of joint angle profiles of the human arm, robotic arm, and box, and the box grasping forces at each time point. A numerical example is simulated for huma-robot lifting with a 10 Kg box. The human and robotic arms’ joint angle, joint torque, and grasping force profiles are reported. These optimal outputs can be used as references to control the human-robot collaborative lifting task. 

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4. An SDP Optimization Formulation for the Inverse Kinematics Problem

   https://doi.org/10.1109/CDC49753.2023.10384035  

   Wu, Liangting; Tron, Roberto (December 2023, IEEE International Conference on Decision and Control)

   Inverse kinematics (IK) is an important problem in robot control and motion planning; however, the nonlinearity of the map from joint angles to robot configurations makes the problem nonconvex. In this paper, we propose an inverse kinematics solver that works in the space of rotation matrices of the link reference frames rather than joint angles. To overcome the nonlinearity of the manifold of rotation matrices $$\mathbf{SO}(3)$$, we propose a semidefinite programming (SDP) relaxation of the kinematic constraints followed by a fixed-trace rank minimization via maximization of a convex function. Along the way, we show that the feasible set of an IK problem is exactly the intersection of a convex set and fixed-trace rank-1 matrices. Thanks to the use of matrices with fixed trace, our algorithm to obtain rank-1 solutions has guaranteed local convergence. Unlike some traditional solvers, our method does not require an initial guess, and can be applied to robots with closed kinematic chains without ad-hoc modifications such as splitting the kinematic chain. Compared to other work that performs SDP relaxation for IK problems, our formulation is simpler, and uses variables with smaller sizes. We validate our approach via simulations on a closed kinematic chain constituted by two robotic arms holding a box, comparing against a standard IK method. 

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5. Learning Navigation Costs from Demonstration with Semantic Observations

   Wang, T.; Dhiman, V.; Atanasov, N. (January 2020, Learning for Dynamics and Control)

   This paper focuses on inverse reinforcement learning (IRL) for autonomous robot navigation using semantic observations. The objective is to infer a cost function that explains demonstrated behavior while relying only on the expert’s observations and state-control trajectory. We develop a map encoder, which infers semantic class probabilities from the observation sequence, and a cost encoder, defined as deep neural network over the semantic features. Since the expert cost is not directly observable, the representation parameters can only be optimized by differentiating the error between demonstrated controls and a control policy computed from the cost estimate. The error is optimized using a closed-form subgradient computed only over a subset of promising states via a motion planning algorithm. We show that our approach learns to follow traffic rules in the autonomous driving CARLA simulator by relying on semantic observations of cars, sidewalks and road lanes. 

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