(Rex) Devon Hjelm

Alexander T Toshev

2025-01-22

ICLR.cc/2025/Conference (poster)

openreview.net

From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessons

Andrew Szot

Omar Attia

Aleksei Timofeev

Harsh Agrawal

Zhe Gan

Zsolt Kira

Alexander T Toshev

We examine the capability of Multimodal Large Language Models (MLLMs) to tackle diverse domains that extend beyond the traditional language … (voir plus)and vision tasks these models are typically trained on. Specifically, our focus lies in areas such as Embodied AI, Games, UI Control, and Planning. To this end, we introduce a process of adapting an MLLM to a Generalist Embodied Agent (GEA). GEA is a single unified model capable of grounding itself across these varied domains through a multi-embodiment action tokenizer. GEA is trained with supervised learning on a large dataset of embodied experiences and with online RL in interactive simulators. We explore the data and algorithmic choices necessary to develop such a model. Our findings reveal the importance of training with cross-domain data and online RL for building generalist agents. The final GEA model achieves strong generalization performance to unseen tasks across diverse benchmarks compared to other generalist models and benchmark-specific approaches.

2024-12-11

ArXiv (prépublication)

From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessons

Andrew Szot

Omar Attia

Aleksei Timofeev

Harsh Agrawal

Zhe Gan

Zsolt Kira

Alexander T Toshev

We examine the capability of Multimodal Large Language Models (MLLMs) to tackle diverse domains that extend beyond the traditional language … (voir plus)and vision tasks these models are typically trained on. Specifically, our focus lies in areas such as Embodied AI, Games, UI Control, and Planning. To this end, we introduce a process of adapting an MLLM to a Generalist Embodied Agent (GEA). GEA is a single unified model capable of grounding itself across these varied domains through a multi-embodiment action tokenizer. GEA is trained with supervised learning on a large dataset of embodied experiences and with online RL in interactive simulators. We explore the data and algorithmic choices necessary to develop such a model. Our findings reveal the importance of training with cross-domain data and online RL for building generalist agents. The final GEA model achieves strong generalization performance to unseen tasks across diverse benchmarks compared to other generalist models and benchmark-specific approaches.

2024-12-11

ArXiv (prépublication)

From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessons

Andrew Szot

Omar Attia

Aleksei Timofeev

Harsh Agrawal

Zhe Gan

Zsolt Kira

Alexander T Toshev

We examine the capability of Multimodal Large Language Models (MLLMs) to tackle diverse domains that extend beyond the traditional language … (voir plus)and vision tasks these models are typically trained on. Specifically, our focus lies in areas such as Embodied AI, Games, UI Control, and Planning. To this end, we introduce a process of adapting an MLLM to a Generalist Embodied Agent (GEA). GEA is a single unified model capable of grounding itself across these varied domains through a multi-embodiment action tokenizer. GEA is trained with supervised learning on a large dataset of embodied experiences and with online RL in interactive simulators. We explore the data and algorithmic choices necessary to develop such a model. Our findings reveal the importance of training with cross-domain data and online RL for building generalist agents. The final GEA model achieves strong generalization performance to unseen tasks across diverse benchmarks compared to other generalist models and benchmark-specific approaches.

2024-12-11

ArXiv (prépublication)

Grounding Multimodal Large Language Models in Actions

Andrew Szot

Harsh Agrawal

Zsolt Kira

Alexander T Toshev

Multimodal Large Language Models (MLLMs) have demonstrated a wide range of capabilities across many domains including Embodied AI. In this w… (voir plus)ork, we study how to best ground a MLLM into different embodiments and their associated action spaces, including both continuous and discrete actions. For continuous actions, a set of learned tokenizations that capture an action at various resolutions allows for sufficient modeling precision, yielding the best performance on downstream tasks. For discrete actions, semantically aligning these actions with the native output token space of the MLLM leads to the strongest performance. We arrive at these lessons via a thorough study of seven action grounding approaches on five different environments, encompassing over 114 embodied tasks.

2024-09-25

NeurIPS.cc/2024/Conference (poster)

openreview.net

Grounding Multimodal Large Language Models in Actions

Andrew Szot

Harsh Agrawal

Zsolt Kira

Alexander T Toshev

2024-06-12

ArXiv (prépublication)

Grounding Multimodal Large Language Models in Actions

Andrew Szot

Harsh Agrawal

Zsolt Kira

Alexander T Toshev

Multimodal Large Language Models (MLLMs) have demonstrated a wide range of capabilities across many domains, including Embodied AI. In this … (voir plus)work, we study how to best ground a MLLM into different embodiments and their associated action spaces, with the goal of leveraging the multimodal world knowledge of the MLLM. We first generalize a number of methods through a unified architecture and the lens of action space adaptors. For continuous actions, we show that a learned tokenization allows for sufficient modeling precision, yielding the best performance on downstream tasks. For discrete actions, we demonstrate that semantically aligning these actions with the native output token space of the MLLM leads to the strongest performance. We arrive at these lessons via a thorough study of seven action space adapters on five different environments, encompassing over 114 embodied tasks.

2024-06-12

ArXiv (prépublication)

Generative Models for Decision Making

Lisa Lee

Roberta Raileanu

Yilun Du

Walter Talbott

Katherine Metcalf