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Found 21 papers, 1 papers with code
Anticipate & Collab: Data-driven Task Anticipation and Knowledge-driven Planning for Human-robot Collaboration
Specifically, DaTAPlan planner computes actions for an agent and a human to collaboratively and jointly achieve the tasks anticipated by the LLM, and the agent automatically adapts to unexpected changes in human action outcomes and preferences.
Relevance Score: A Landmark-Like Heuristic for Planning
We describe an approach to compute this relevance score and use it as a heuristic in the search for a plan.
Knowledge-based Reasoning and Learning under Partial Observability in Ad Hoc Teamwork
They use a large labeled dataset of prior observations to model the behavior of other agent types and to determine the ad hoc agent's behavior.
Sequence-Agnostic Multi-Object Navigation
The Multi-Object Navigation (MultiON) task requires a robot to localize an instance (each) of multiple object classes.
Object Goal Navigation using Data Regularized Q-Learning
Our framework incrementally builds a semantic map of the environment over time, and then repeatedly selects a long-term goal ('where to go') based on the semantic map to locate the target object instance.
Spatial Relation Graph and Graph Convolutional Network for Object Goal Navigation
This paper describes a framework for the object-goal navigation task, which requires a robot to find and move to the closest instance of a target object class from a random starting position.
Knowledge-based and Data-driven Reasoning and Learning for Ad Hoc Teamwork
We present an architecture for ad hoc teamwork, which refers to collaboration in a team of agents without prior coordination.
A Survey of Ad Hoc Teamwork Research
Ad hoc teamwork is the research problem of designing agents that can collaborate with new teammates without prior coordination.
Combining Commonsense Reasoning and Knowledge Acquisition to Guide Deep Learning in Robotics
Algorithms based on deep network models are being used for many pattern recognition and decision-making tasks in robotics and AI.
The Ninth Advances in Cognitive Systems (ACS) Conference
ACS is an annual meeting for research on the initial goals of artificial intelligence and cognitive science, which aimed to explain the mind in computational terms and to reproduce the entire range of human cognitive abilities in computational artifacts.
Towards a Framework for Changing-Contact Robot Manipulation
We present a framework for smooth dynamics and control of such changing-contact manipulation tasks.
Continual Learning of Knowledge Graph Embeddings
In recent years, there has been a resurgence in methods that use distributed (neural) representations to represent and reason about semantic knowledge for robotics applications.
Axiom Learning and Belief Tracing for Transparent Decision Making in Robotics
A robot's ability to provide descriptions of its decisions and beliefs promotes effective collaboration with humans.
A Survey of Knowledge-based Sequential Decision Making under Uncertainty
Reasoning with declarative knowledge (RDK) and sequential decision-making (SDM) are two key research areas in artificial intelligence.
Non-monotonic Logical Reasoning Guiding Deep Learning for Explainable Visual Question Answering
In the context of answering explanatory questions about scenes and the underlying classification problems, the architecture uses deep networks for extracting features from images and for generating answers to queries.
Towards a Theory of Intentions for Human-Robot Collaboration
Each abstract action is implemented as a sequence of concrete actions by automatically zooming to and reasoning with the part of the fine-resolution transition diagram relevant to the current coarse-resolution transition and the goal.
Generative grasp synthesis from demonstration using parametric mixtures
We present a parametric formulation for learning generative models for grasp synthesis from a demonstration.
Robotics
REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics
This paper describes an architecture for robots that combines the complementary strengths of probabilistic graphical models and declarative programming to represent and reason with logic-based and probabilistic descriptions of uncertainty and domain knowledge.
Mixed Logical and Probabilistic Reasoning for Planning and Explanation Generation in Robotics
The architecture described in this paper couples the non-monotonic logical reasoning capabilities of a declarative language with probabilistic belief revision, enabling robots to represent and reason with qualitative and quantitative descriptions of knowledge and degrees of belief.
KR$^3$: An Architecture for Knowledge Representation and Reasoning in Robotics
This paper describes an architecture that combines the complementary strengths of declarative programming and probabilistic graphical models to enable robots to represent, reason with, and learn from, qualitative and quantitative descriptions of uncertainty and knowledge.
Combining Answer Set Programming and POMDPs for Knowledge Representation and Reasoning on Mobile Robots
For widespread deployment in domains characterized by partial observability, non-deterministic actions and unforeseen changes, robots need to adapt sensing, processing and interaction with humans to the tasks at hand.
