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Found 5 papers, 1 papers with code
GROUNDHOG: Grounding Large Language Models to Holistic Segmentation
Most multimodal large language models (MLLMs) learn language-to-object grounding through causal language modeling where grounded objects are captured by bounding boxes as sequences of location tokens.
Ranked #1 on
Generalized Referring Expression Segmentation
on gRefCOCO
(using extra training data)
Causal Language Modeling
Generalized Referring Expression Segmentation
+2
Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning
Inspired by their success in language tasks, existing research has leveraged LLMs in embodied instruction following and task planning.
Alexa, play with robot: Introducing the First Alexa Prize SimBot Challenge on Embodied AI
The Alexa Prize program has empowered numerous university students to explore, experiment, and showcase their talents in building conversational agents through challenges like the SocialBot Grand Challenge and the TaskBot Challenge.
LEMMA: Learning Language-Conditioned Multi-Robot Manipulation
We introduce a benchmark for LanguagE-Conditioned Multi-robot MAnipulation (LEMMA) focused on task allocation and long-horizon object manipulation based on human language instructions in a tabletop setting.
Alexa Arena: A User-Centric Interactive Platform for Embodied AI
We introduce Alexa Arena, a user-centric simulation platform for Embodied AI (EAI) research.
