REVIEW 2 cited by
Enabling Waypoint Generation for Collaborative Robots using LLMs and Mixed Reality
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Programming a robotic is a complex task, as it demands the user to have a good command of specific programming languages and awareness of the robot's physical constraints. We propose a framework that simplifies robot deployment by allowing direct communication using natural language. It uses large language models (LLM) for prompt processing, workspace understanding, and waypoint generation. It also employs Augmented Reality (AR) to provide visual feedback of the planned outcome. We showcase the effectiveness of our framework with a simple pick-and-place task, which we implement on a real robot. Moreover, we present an early concept of expressive robot behavior and skill generation that can be used to communicate with the user and learn new skills (e.g., object grasping).
Forward citations
Cited by 2 Pith papers
-
ORCESTRA: VLM-driven Visual Robot programming in Mixed Reality
A mixed-reality system combines no-code waypoint teaching and VLM-guided language control for programming heterogeneous robot digital twins with confirmation-gated execution.
-
LLMER: Crafting Interactive Extended Reality Worlds with JSON Data Generated by Large Language Models
LLMER uses LLM-generated JSON data instead of code to create interactive XR worlds, cutting token use and task completion time in a small user study.
Discussion (0). Continue with ORCID to comment.