Going Beyond Literal Command-Based Instructions: Extending Robotic Natural Language Interaction Capabilities

Authors: Tom Williams, Gordon Briggs, Bradley Oosterveld, Matthias Scheutz

AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We demonstrate the potential of these inference algorithms for natural human-robot interactions by running them as part of an integrated cognitive robotic architecture on a mobile robot in a dialoguebased instruction task. and To demonstrate the operation of the proposed inference algorithms for natural human-robot interactions, we consider a dialogue interaction that occurs as part of a Search-and Rescue Task.
Researcher Affiliation Academia Tom Williams, Gordon Briggs, Bradley Oosterveld, and Matthias Scheutz Human-Robot Interaction Laboratory Tufts University, Medford, MA, USA {thomas_e.williams, gordon.briggs, bradley.oosterveld, matthias.scheutz}@tufts.edu
Pseudocode Yes Algorithm 1 get Intended Meaning({ΘU,mu},{ΘC,mc},R) and Algorithm 2 get Semantics({ΘI,mi},{ΘC,mc},R)
Open Source Code No The paper only provides a link to a video demonstration: 'A video of this interaction in operation on a Willow Garage PR2 robot can be viewed at https://vimeo.com/106203678.'. There is no explicit statement or link for the source code of the methodology described in the paper.
Open Datasets No The paper describes a demonstration scenario with predefined conditions and pragmatic rules for a 'Search-and Rescue Task', but it does not specify a publicly available or open dataset for training or evaluation.
Dataset Splits No The paper describes a demonstration and specific scenarios (Ulow, Umed, Uhigh, Cjim, Crobot, Cunk) with predefined parameters and rules, but it does not provide specific dataset split information (e.g., percentages or sample counts) for training, validation, or testing.
Hardware Specification Yes A video of this interaction in operation on a Willow Garage PR2 robot can be viewed at https://vimeo.com/106203678.
Software Dependencies No The paper mentions using the DIARC architecture and Dempster-Shafer theory but does not provide specific software dependencies with version numbers (e.g., library names with versions like 'Python 3.8' or 'PyTorch 1.9').
Experiment Setup Yes All beliefs and plausibilities listed in this section are rounded to two decimal places for the reader s convenience. and Ulow (with uncertainty interval [0.95, 1.00]), Umed (with uncertainty interval [0.62, 0.96]), and Uhigh (with uncertainty interval [0.31, 0.81]). and We introduce an uncertainty threshold Λ (set to 0.1)