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Michael Lingelbach

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5 papers

2 author rows

NeurIPS Conference 2024 Conference Paper

Phased Consistency Models

Fu-Yun Wang
Zhaoyang Huang
Alexander W. Bergman
Dazhong Shen
Peng Gao
Michael Lingelbach
Keqiang Sun
Weikang Bian

Consistency Models (CMs) have made significant progress in accelerating the generation of diffusion models. However, their application to high-resolution, text-conditioned image generation in the latent space remains unsatisfactory. In this paper, we identify three key flaws in the current design of Latent Consistency Models~(LCMs). We investigate the reasons behind these limitations and propose Phased Consistency Models (PCMs), which generalize the design space and address the identified limitations. Our evaluations demonstrate that PCMs outperform LCMs across 1--16 step generation settings. While PCMs are specifically designed for multi-step refinement, they achieve comparable 1-step generation results to previously state-of-the-art specifically designed 1-step methods. Furthermore, we show the methodology of PCMs is versatile and applicable to video generation, enabling us to train the state-of-the-art few-step text-to-video generator. Our code is available at https: //github. com/G-U-N/Phased-Consistency-Model.

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AAMAS Conference 2023 Conference Paper

Modeling Dynamic Environments with Scene Graph Memory

Andrey Kurenkov
Michael Lingelbach
Tanmay Agarwal
Chengshu Li
Emily Jin
Ruohan Zhang
Li Fei-Fei
Jiajun Wu

Embodied AI agents operating in dynamic environments often need to predict object locations to make informed decisions. We propose a method for doing this via link prediction on partially observable dynamic graphs. We represent the agent’s accumulated set of observations in a data structure called a Scene Graph Memory (SGM), combine this data structure with a neural net architecture we call Node Edge Predictor (NEP), and show that it can be trained to predict the locations of objects in a variety of environments with diverse object movement dynamics. To evaluate our method, we implement the Dynamic Household Simulator, a novel benchmark which enables sampling of diverse dynamic scene graphs that follow the semantic patterns typically seen at peoples’ homes. We demonstrate that our method outperforms baselines both in terms of quickly adapting to the dynamics of a new scene and in terms of its overall accuracy.

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ICML Conference 2023 Conference Paper

Modeling Dynamic Environments with Scene Graph Memory

Andrey Kurenkov
Michael Lingelbach
Tanmay Agarwal
Emily Jin
Chengshu Li 0002
Ruohan Zhang
Li Fei-Fei 0001
Jiajun Wu 0001

Embodied AI agents that search for objects in large environments such as households often need to make efficient decisions by predicting object locations based on partial information. We pose this as a new type of link prediction problem: link prediction on partially observable dynamic graphs Our graph is a representation of a scene in which rooms and objects are nodes, and their relationships are encoded in the edges; only parts of the changing graph are known to the agent at each timestep. This partial observability poses a challenge to existing link prediction approaches, which we address. We propose a novel state representation – Scene Graph Memory (SGM) – with captures the agent’s accumulated set of observations, as well as a neural net architecture called a Node Edge Predictor (NEP) that extracts information from the SGM to search efficiently. We evaluate our method in the Dynamic House Simulator, a new benchmark that creates diverse dynamic graphs following the semantic patterns typically seen at homes, and show that NEP can be trained to predict the locations of objects in a variety of environments with diverse object movement dynamics, outperforming baselines both in terms of new scene adaptability and overall accuracy. The codebase and more can be found www. scenegraphmemory. com.

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ICRA Conference 2023 Conference Paper

Task-Driven Graph Attention for Hierarchical Relational Object Navigation

Michael Lingelbach
Chengshu Li 0002
Minjune Hwang
Andrey Kurenkov
Alan Lou
Roberto Martín-Martín
Ruohan Zhang
Li Fei-Fei 0001

Embodied AI agents in large scenes often need to navigate to find objects. In this work, we study a naturally emerging variant of the object navigation task, hierarchical relational object navigation (HRON), where the goal is to find objects specified by logical predicates organized in a hierarchical structure-objects related to furniture and then to rooms-such as finding an apple on top of a table in the kitchen. Solving such a task requires an efficient representation to reason about object relations and correlate the relations in the environment and in the task goal. HRON in large scenes (e. g. homes) is particularly challenging due to its partial observability and long horizon, which invites solutions that can compactly store the past information while effectively exploring the scene. We demonstrate experimentally that scene graphs are the best-suited representation compared to conventional representations such as images or 2D maps. We propose a solution that uses scene graphs as part of its input and integrates graph neural networks as its backbone, with an integrated task-driven attention mechanism, and demonstrate its better scalability and learning efficiency than state-of-the-art baselines.

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NeurIPS Conference 2021 Conference Paper

ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation

Chuang Gan
Jeremy Schwartz
Seth Alter
Damian Mrowca
Martin Schrimpf
James Traer
Julian De Freitas
Jonas Kubilius

We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation. TDW enables the simulation of high-fidelity sensory data and physical interactions between mobile agents and objects in rich 3D environments. Unique properties include real-time near-photo-realistic image rendering; a library of objects and environments, and routines for their customization; generative procedures for efficiently building classes of new environments; high-fidelity audio rendering; realistic physical interactions for a variety of material types, including cloths, liquid, and deformable objects; customizable ``avatars” that embody AI agents; and support for human interactions with VR devices. TDW’s API enables multiple agents to interact within a simulation and returns a range of sensor and physics data representing the state of the world. We present initial experiments enabled by TDW in emerging research directions in computer vision, machine learning, and cognitive science, including multi-modal physical scene understanding, physical dynamics predictions, multi-agent interactions, models that ‘learn like a child’, and attention studies in humans and neural networks.

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