Gregory Dudek

MANSION: Multi-floor lANguage-to-3D Scene generatIOn for loNg-horizon tasks

Lirong Che

Shuo Wen

Shan Huang

Chuang Wang

Yuzhe Yang

Xueqian Wang

Jian Su

Real-world robotic tasks are long-horizon and often span multiple floors, demanding rich spatial reasoning. However, existing embodied bench… (voir plus)marks are largely confined to single-floor in-house environments, failing to reflect the complexity of real-world tasks. We introduce MANSION, the first language-driven framework for generating building-scale, multi-floor 3D environments. Being aware of vertical structural constraints, MANSION generates realistic, navigable whole-building structures with diverse, human-friendly scenes, enabling the development and evaluation of cross-floor long-horizon tasks. Building on this framework, we release MansionWorld, a dataset of over 1,000 diverse buildings ranging from hospitals to offices, alongside a Task-Semantic Scene Editing Agent that customizes these environments using open-vocabulary commands to meet specific user needs. Benchmarking reveals that state-of-the-art agents degrade sharply in our settings, establishing MANSION as a critical testbed for the next generation of spatial reasoning and planning.

2026-03-11

arXiv (prépublication)

AMP2026: A Multi-Platform Marine Robotics Dataset for Tracking and Mapping

Edwin Meriaux

Shuo Wen

David Widhalm

Zhizun Wang

Junming Shi

Mariana Sosa Guzmán

Kalvik Jakkala

Bennett Carley

Elias Sokolova

Yogesh Girdhar

Monika Roznere

Jason O’Kane

Junaed Sattar

Marine environments present significant challenges for perception and autonomy due to dynamic surfaces, limited visibility, and complex inte… (voir plus)ractions between aerial, surface, and submerged sensing modalities. This paper introduces the Aerial Marine Perception Dataset (AMP2026), a multi-platform marine robotics dataset collected across multiple field deployments designed to support research in two primary areas: multi-view tracking and marine environment mapping. The dataset includes synchronized data from aerial drones, boat-mounted cameras, and submerged robotic platforms, along with associated localization and telemetry information. The goal of this work is to provide a publicly available dataset enabling research in marine perception and multi-robot observation scenarios. This paper describes the data collection methodology, sensor configurations, dataset organization, and intended research tasks supported by the dataset.

2026-03-03

arXiv (prépublication)

Contractive Diffusion Policies

Charlotte Morissette

Diffusion policies have emerged as powerful generative models for offline policy learning, whose sampling process can be rigorously characte… (voir plus)rized by a score function guiding a Stochastic Differential Equation (SDE). However, the same score-based SDE modeling that grants diffusion policies the flexibility to learn diverse behavior also incurs solver and score-matching errors, large data requirements, and inconsistencies in action generation. While less critical in image generation, these inaccuracies compound and lead to failure in continuous control settings. We introduce **C**ontractive **D**iffusion **P**olicies (CDPs) to induce contractive behavior in the diffusion sampling dynamics. Contraction pulls nearby flows closer to enhance robustness against solver and score-matching errors while reducing unwanted action variance. We develop an in-depth theoretical analysis along with a practical implementation recipe to incorporate CDPs into existing diffusion policy architectures with minimal modification and computational cost. We evaluate CDPs for offline learning by conducting extensive experiments in simulation and real world settings. Across benchmarks, CDPs often outperform baseline policies, with pronounced benefits under data scarcity. Project page: https://contractive-diffusion.github.io

2026-01-25

International Conference on Learning Representations (poster)

openreview.net

Contractive Diffusion Policies: Robust Action Diffusion via Contractive Score-Based Sampling with Differential Equations

Charlotte Morissette

Anas El Houssaini

Diffusion policies have emerged as powerful generative models for offline policy learning, whose sampling process can be rigorously characte… (voir plus)rized by a score function guiding a Stochastic Differential Equation (SDE). However, the same score-based SDE modeling that grants diffusion policies the flexibility to learn diverse behavior also incurs solver and score-matching errors, large data requirements, and inconsistencies in action generation. While less critical in image generation, these inaccuracies compound and lead to failure in continuous control settings. We introduce Contractive Diffusion Policies (CDPs) to induce contractive behavior in the diffusion sampling dynamics. Contraction pulls nearby flows closer to enhance robustness against solver and score-matching errors while reducing unwanted action variance. We develop an in-depth theoretical analysis along with a practical implementation recipe to incorporate CDPs into existing diffusion policy architectures with minimal modification and computational cost. We evaluate CDPs for offline learning by conducting extensive experiments in simulation and real-world settings. Across benchmarks, CDPs often outperform baseline policies, with pronounced benefits under data scarcity.

2025-12-31

arXiv (prépublication)

openreview.net

MANSION: Multi-floor lANguage-to-3D Scene generatIOn for loNg-horizon tasks

Lirong Che

Shuo Wen

Shan Huang

Chuang Wang

Yuzhe Yang

Xueqian Wang

Jian Su

Real-world robotic tasks are long-horizon and often span multiple floors, requiring complex spatial reasoning. Existing embodied benchmarks,… (voir plus) however, are largely confined to single-floor homes, failing to evaluate agents on realistic, building-scale tasks. We introduce MANSION, a language-driven framework for generating building-scale, multi-floor 3D environments for long-horizon tasks. Using this framework, we release MansionWorld, a large-scale dataset featuring over 1,000 diverse, non-residential buildings. These environments support cross-floor skills and long-horizon task generation on reusable building layouts. Experiments show that current methods degrade sharply on our multi-floor tasks, highlighting both the challenge and the value of this setting for advancing embodied AI.

2025-12-31

IEEE/CVF Conference on Computer Vision and Pattern Recognition (Accept (Poster))

The Surprising Difficulty of Search in Model-Based Reinforcement Learning

Wei-Di Chang

Mikael Henaff

Brandon Amos

Scott Fujimoto

This paper investigates search in model-based reinforcement learning (RL). Conventional wisdom holds that long-term predictions and compound… (voir plus)ing errors are the primary obstacles for model-based RL. We challenge this view, showing that search is not a plug-and-play replacement for a learned policy. Surprisingly, we find that search can harm performance even when the model is highly accurate. Instead, we show that mitigating distribution shift matters more than improving model or value function accuracy. Building on this insight, we identify key techniques for enabling effective search, achieving state-of-the-art performance across multiple popular benchmark domains.

2025-12-31

arXiv (prépublication)

On Mobile Ad Hoc Networks for Coverage of Partially Observable Worlds

Edwin Meriaux

Shuo Wen

Louis-Roy Langevin

Doina Precup

Antonio Lor'ia

2025-11-30

arXiv (publié)

Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning

Andrew Holliday

Ahmed El-Geneidy

2025-10-16

Transportmetrica B: Transport Dynamics (publié)

Large Pre-Trained Models for Bimanual Manipulation in 3D

Hanna Yurchyk

Wei-Di Chang

David Meger

2025-09-29

2025 IEEE-RAS 24th International Conference on Humanoid Robots (Humanoids) (publié)

Generalizable Imitation Learning Through Pre-Trained Representations

Wei-Di Chang

Francois Hogan

David Meger

Scott Fujimoto

In this paper we leverage self-supervised vision transformer models and their emergent semantic abilities to improve the generalization abil… (voir plus)ities of imitation learning policies. We introduce BC-ViT, an imitation learning algorithm that leverages rich DINO pre-trained Visual Transformer (ViT) patch-level embeddings to obtain better generalization when learning through demonstrations. Our learner sees the world by clustering appearance features into semantic concepts, forming stable keypoints that generalize across a wide range of appearance variations and object types. We show that this representation enables generalized behaviour by evaluating imitation learning across a diverse dataset of object manipulation tasks. Our method, data and evaluation approach are made available to facilitate further study of generalization in Imitation Learners.

2025-05-18

2025 IEEE International Conference on Robotics and Automation (ICRA) (publié)

AIoT Smart Home via Autonomous LLM Agents

Dmitriy Rivkin

Francois Hogan

Amal Feriani

Abhisek Konar

Adam Sigal

Xue Liu

2025-01-31

IEEE Internet of Things Journal (publié)