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Charlie Gauthier

Doctorat - UdeM
Superviseur⋅e principal⋅e
Liam Paull
Co-supervisor
Glen Berseth
Sujets de recherche
Apprentissage par renforcement
Apprentissage profond
Neurosciences computationnelles
Vision par ordinateur

Publications

PerceptTwin: Semantic Scene Reconstruction for Iterative LLM Planning and Verification
Charlie Gauthier
Sacha Morin
Liam Paull
Simulation environments are useful for both robot policy learning and planning verification and validation. Traditionally, the process of cr… (voir plus)eating a simulation was onerous. Creating a bespoke simulation environment for each individual environment that a robot would operate in was simply infeasible. In this work, we introduce PerceptTwin, a fully automatic pipeline that constructs interactive simulations directly from semantic scene representations produced by a robot's perception stack. PerceptTwin combines open-vocabulary object maps with 3D asset generation, affordance prediction, and commonsense condition checking. These interactive simulations can be used to validate and refine plans before they are executed on the robot hardware. Borrowing from the AI alignment literature, we also introduce an LLM judge that verifies plan correctness and alignment with human preferences. Experiments show that PerceptTwin feedback allows LLM planners to refine plans, enhance safety, and resist harmful black-box prompting attacks. In our suite of tasks, PerceptTwin improves plan success by an average of approximately 39% for GPT5, GPT5Mini, and GPT5Nano planners. Additionally, PerceptTwin also improves human plan verification by up to 18% on average for plans that fail due to unfilled skill preconditions. Our results demonstrate the potential of open-vocabulary scene simulation from robot perception as a foundation for safer, more reliable robot planning.
2026-06-01
arXiv (prépublication)
doi.org
arxiv.org
Predictive Spatio-Temporal Scene Graphs for Semi-Static Scenes
Miguel Saavedra-Ruiz
Charlie Gauthier
Kumaraditya Gupta
Shima Shahfar
Kirsty Ellis
Steven Parkison
Liam Paull
We have seen tremendous recent progress in our ability to build "spatio-semantic" representations that enable robots to perform complex reas… (voir plus)oning across geometry and semantics. However, the vast majority of these methods lack any ability to perform reasoning across time. This is a desirable property in situations where a robot repeatedly observes an environment where instances may change in between observations, but in a structured way. Consider as an example a home environment where the location of a mug typically moves from the cupboard to a countertop to the sink and then back to the cupboard on a daily basis. We should be able to learn this cyclic behavior and use it to predict the state of the mug in the future. In this work, we propose a method that is able to perform this type of tempo-spatio-semantic reasoning. Underpinning the method is a filter, Perpetua
2026-04-29
arXiv (prépublication)
doi.org
arxiv.org
Perpetua: Multi-Hypothesis Persistence Modeling for Semi-Static Environments
Miguel Saavedra-Ruiz
Samer B. Nashed
Charlie Gauthier
Liam Paull
Many robotic systems require extended deployments in complex, dynamic environments. In such deployments, parts of the environment may change… (voir plus) between subsequent robot observations. Most robotic mapping or environment modeling algorithms are incapable of representing dynamic features in a way that enables predicting their future state. Instead, they opt to filter certain state observations, either by removing them or some form of weighted averaging. This paper introduces Perpetua, a method for modeling the dynamics of semi-static features. Perpetua is able to: incorporate prior knowledge about the dynamics of the feature if it exists, track multiple hypotheses, and adapt over time to enable predicting of future feature states. Specifically, we chain together mixtures of"persistence"and"emergence"filters to model the probability that features will disappear or reappear in a formal Bayesian framework. The approach is an efficient, scalable, general, and robust method for estimating the states of features in an environment, both in the present as well as at arbitrary future times. Through experiments on simulated and real-world data, we find that Perpetua yields better accuracy than similar approaches while also being online adaptable and robust to missing observations.
2025-10-18
2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publié)
doi.org
arxiv.org
Object-Centric Agentic Robot Policies
Sacha Morin
Kumaraditya Gupta
Mahtab Sandhu
Charlie Gauthier
Francesco Argenziano
Kirsty Ellis
Liam Paull
Executing open-ended natural language queries in previously unseen environments is a core problem in robotics. While recent advances in imit… (voir plus)ation learning and vision-language modeling have enabled promising end-to-end policies, these models struggle when faced with complex instructions and new scenes. Their short input context also limits their ability to solve tasks over larger spatial horizons. In this work, we introduce OCARP, a modular agentic robot policy that executes user queries by using a library of tools on a dynamic inventory of objects. The agent builds the inventory by grounding query-relevant objects using a rich 3D map representation that includes open-vocabulary descriptors and 3D affordances. By combining the flexible reasoning abilities of an agent with a general spatial representation, OCARP can execute complex open-vocabulary queries in a zero-shot manner. We showcase how OCARP can be deployed in both tabletop and mobile settings due to the underlying scalable map representation.
2025-09-22
NeurIPS.cc/2025/Workshop/SpaVLE (poster)
openreview.net
openreview.net
Safety Representations for Safer Policy Learning
Kaustubh Mani
Vincent Mai
Charlie Gauthier
Annie Chen
Samer Nashed
Liam Paull
Reinforcement learning algorithms typically necessitate extensive exploration of the state space to find optimal policies. However, in safet… (voir plus)y-critical applications, the risks associated with such exploration can lead to catastrophic consequences. Existing safe exploration methods attempt to mitigate this by imposing constraints, which often result in overly conservative behaviours and inefficient learning. Heavy penalties for early constraint violations can trap agents in local optima, deterring exploration of risky yet high-reward regions of the state space. To address this, we introduce a method that explicitly learns state-conditioned safety representations. By augmenting the state features with these safety representations, our approach naturally encourages safer exploration without being excessively cautious, resulting in more efficient and safer policy learning in safety-critical scenarios. Empirical evaluations across diverse environments show that our method significantly improves task performance while reducing constraint violations during training, underscoring its effectiveness in balancing exploration with safety.
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net