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Guillaume Adrien Sartoretti

13 accepted papers

2026

Demystifying Robot Diffusion Policies: Action Memorization and a Simple Lookup Table Alternative

Chengyang He, Xu Liu, Gadiel Mark Sznaier Camps, Joseph Bruno, Guillaume Adrien Sartoretti, Mac Schwager
ICLR 2026poster

Diffusion policies for visuomotor robot manipulation tasks achieve remarkable dexterity and robustness while only training on a small number of task demonstrations. However, the reason for this performance remains a mystery. In this paper, we offer a surprising hypothesis: diffusion policies essent…

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2026

GRATE: A Graph Transformer-Based Deep Reinforcement Learning Approach for Time-Efficient Autonomous Robot Exploration

Haozhan Ni, Jingsong Liang, Chenyu He, Yuhong Cao, Guillaume Adrien Sartoretti
ICRA 2026poster

Autonomous robot exploration (ARE) is the process of a robot autonomously navigating and mapping an unknown environment. Recent Reinforcement Learning (RL)-based approaches typically formulate ARE as a sequential decision-making problem defined on a collision-free informative graph. However, these m…

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2026

P3GASUS: Pre-Planned Path Execution Graphs for Multi-Agent Systems at Ultra-Large Scale

Tanishq Harish Duhan, Chengyang He, Guillaume Adrien Sartoretti
ICRA 2026poster

Executing pre-planned paths in multi-agent systems is challenging, as a lack of synchronization can lead to collisions or live-/deadlocks, while enforcing strict synchronization may cause a widespread team delay in reaching goals. An Action Dependency Graph (ADG) solves this problem by identifying a…

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2025

CogniPlan: Uncertainty-Guided Path Planning with Conditional Generative Layout Prediction

Yizhuo Wang, Haodong He, Jingsong Liang, Yuhong Cao, Ritabrata Chakraborty, Guillaume Adrien Sartoretti
CoRL 2025poster

Path planning in unknown environments is a crucial yet inherently challenging capability for mobile robots, which primarily encompasses two coupled tasks: autonomous exploration and point-goal navigation. In both cases, the robot must perceive the environment, update its belief, and accurately estim…

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2025

Latent Theory of Mind: A Decentralized Diffusion Architecture for Cooperative Manipulation

Chengyang He, Gadiel Mark Sznaier Camps, Xu Liu, Mac Schwager, Guillaume Adrien Sartoretti
CoRL 2025oral

We present Latent Theory of Mind (LatentToM), a decentralized diffusion policy architecture for collaborative robot manipulation. Our policy allows multiple manipulators with their own perception and computation to collaborate with each other towards a common task goal with or without explicit commu…

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2025

Multimodal Fused Learning for Solving the Generalized Traveling Salesman Problem in Robotic Task Planning

Jiaqi Cheng, Mingfeng Fan, Xuefeng Zhang, Jingsong Liang, Yuhong Cao, Guohua Wu +1
CoRL 2025poster

Effective and efficient task planning is essential for mobile robots, especially in applications like warehouse retrieval and environmental monitoring. These tasks often involve selecting one location from each of several target clusters, forming a Generalized Traveling Salesman Problem (GTSP) that…

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2025

Preference-Driven Multi-Objective Combinatorial Optimization with Conditional Computation

Mingfeng Fan, Jianan Zhou, Yifeng Zhang, Yaoxin Wu, Jinbiao Chen, Guillaume Adrien Sartoretti
NeurIPS 2025poster

Recent deep reinforcement learning methods have achieved remarkable success in solving multi-objective combinatorial optimization problems (MOCOPs) by decomposing them into multiple subproblems, each associated with a specific weight vector. However, these methods typically treat all subproblems equ…

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2025

SATA: Safe and Adaptive Torque-Based Locomotion Policies Inspired by Animal Learning

Li Peizhuo, Hongyi Li, Ge Sun, Jin Cheng, Xinrong Yang, Guillaume Bellegarda +4
RSS 2025poster

Despite recent advances in learning-based controllers for legged robots, deployments in human-centric environments remain limited by safety concerns. Most of these approaches use position-based control, where policies output target joint angles that must be processed by a low-level controller (e.g.,…

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2025

Search-TTA: A Multi-Modal Test-Time Adaptation Framework for Visual Search in the Wild

Derek Ming Siang Tan, Shailesh, Boyang Liu, Alok Raj, Qi Xuan Ang, Weiheng Dai +5
CoRL 2025poster

To perform autonomous visual search for environmental monitoring, a robot may leverage satellite imagery as a prior map. This can help inform coarse, high level search and exploration strategies, even when such images lack sufficient resolution to allow fine-grained, explicit visual recognition of t…

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2024

ViPER: Visibility-based Pursuit-Evasion via Reinforcement Learning

Yizhuo Wang, Yuhong Cao, Jimmy Chiun, Subhadeep Koley, Mandy Pham, Guillaume Adrien Sartoretti
CoRL 2024poster

In visibility-based pursuit-evasion tasks, a team of mobile pursuer robots with limited sensing capabilities is tasked with detecting all evaders in a multiply-connected planar environment, whose map may or may not be known to pursuers beforehand. This requires tight coordination among multiple agen…

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2023

Context-Aware Deep Reinforcement Learning for Autonomous Robotic Navigation in Unknown Area

Jingsong Liang, Zhichen Wang, Yuhong Cao, Jimmy Chiun, Mengqi Zhang, Guillaume Adrien Sartoretti
CoRL 2023poster

Mapless navigation refers to a challenging task where a mobile robot must rapidly navigate to a predefined destination using its partial knowledge of the environment, which is updated online along the way, instead of a prior map of the environment. Inspired by the recent developments in deep reinfor…

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2023

Learning Temporally AbstractWorld Models without Online Experimentation

Benjamin Freed, Siddarth Venkatraman, Guillaume Adrien Sartoretti, Jeff Schneider, Howie Choset
ICML 2023poster

Agents that can build temporally abstract representations of their environment are better able to understand their world and make plans on extended time scales, with limited computational power and modeling capacity. However, existing methods for automatically learning temporally abstract world mode…

Cited by 8Source ↗PDF ↗Scholar ↗
2022

CAtNIPP: Context-Aware Attention-based Network for Informative Path Planning

Yuhong Cao, Yizhuo Wang, Apoorva Vashisth, Haolin Fan, Guillaume Adrien Sartoretti
CoRL 2022poster

Informative path planning (IPP) is an NP-hard problem, which aims at planning a path allowing an agent to build an accurate belief about a quantity of interest throughout a given search domain, within constraints on resource budget (e.g., path length for robots with limited battery life). IPP requir…

Cited by 32Source ↗Scholar ↗