IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents

Aleksandr I. Panov; Alexey Skrynnik; Artem Zholus; Artur Szlam; Julia Kiseleva; Marc-Alexandre Cot\'e; Shrestha Mohanty; Zoya Volovikova

IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

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

arxiv 2206.00142 v1 pith:SPH2VJ2H submitted 2022-05-31 cs.LG cs.AIcs.CL

Artem Zholus , Alexey Skrynnik , Shrestha Mohanty , Zoya Volovikova , Julia Kiseleva , Artur Szlam , Marc-Alexandre Coté , Aleksandr I. Panov This is my paper · ORCID

classification cs.LGcs.AIcs.CL

keywords environmentagentsbuildingconditionedembodiedgridworldiglulanguage

verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments

read the original abstract

We present the IGLU Gridworld: a reinforcement learning environment for building and evaluating language conditioned embodied agents in a scalable way. The environment features visual agent embodiment, interactive learning through collaboration, language conditioned RL, and combinatorically hard task (3d blocks building) space.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Code-Driven Planning in Grid Worlds with Large Language Models
cs.AI 2025-05 conditional novelty 4.0 of 10

An iterative code-generation framework (IPP) improves LLM performance on GRASP and MiniGrid grid-planning tasks by refining generated policy programs based on execution feedback.

Pith tools