Personalized and Robust Proactive Robot Assistance with Uncertainty-Guided LLM Reasoning

summary

Video file (mp4)

The gist

The gist: GLOBE, a lightweight framework combining n-gram Markov models for temporal behavioral patterns with uncertainty-guided large language model (LLM) reasoning, achieves competitive performance

In short

GLOBE is a lightweight framework for proactive robot assistance that predicts human activities and object usage in homes. It combines simple n-gram Markov models for patterns with large language model (LLM) reasoning, which is used only when the system is uncertain. This method achieves competitive performance while significantly improving robustness against environmental noise.

Key concepts

n-gram Markov models
These are statistical models used to predict the next event based on a sequence of past events. In this paper, they capture temporal behavioral patterns by counting how often one activity follows another, helping the robot guess what the user will do next in a household setting.
Uncertainty-Guided LLM Reasoning
This concept uses a powerful language model (LLM) as a backup. The system first tries to predict things using simpler models; if it is unsure about the prediction, it asks the LLM for help. The LLM then provides deeper, context-aware reasoning to refine the guess.
HOMER-Noise
This is a special dataset extension that simulates real household disturbances. It takes existing data and adds controlled noise like movements from pets or toddlers. This allows the robot's prediction system to be tested in more realistic, messy environments.
Sequential Prediction Framework
The robot predicts things in steps: first predicting the next activity using n-grams, and then predicting which objects are likely to be used during that specific activity. This two-step process ensures the object predictions make sense given the predicted action.

Terminology used across episodes

This episode discusses

The paper

Personalized and Robust Proactive Robot Assistance with Uncertainty-Guided LLM Reasoning · Read on arXiv

Alvaro Gonzalez, §M.H. Hasan Shovo, Ali Ayub

Concordia University

Proactive robot assistance in household environments requires accurate prediction of human activities and object usage under dynamic and noisy conditions. Existing approaches often rely on complex spatio-temporal models, which can be computationally expensive and sensitive to environmental variability. In this paper, we propose GLOBE, a lightweight framework that combines n-gram Markov models for capturing temporal behavioral patterns with uncertainty-guided large language model (LLM) reasoning. The framework performs sequential prediction efficiently while selectively invoking LLM reasoning only when the model confidence is low. To evaluate performance under realistic conditions, we introduce HOMER-Noise, a noisy extension of the HOMER+ dataset that simulates structured disturbances such as object movements caused by humans, pets, and toddlers. Experimental results show that GLOBE achieves competitive performance with state-of-the-art methods while improving robustness and computational efficiency across both clean and noisy settings. The framework is further validated through a proof-of-concept integration with a Stretch 3 mobile manipulator, demonstrating its potential application in real-world human-robot interaction scenarios.

DOI: 10.1109/RO-MAN72286.2026.11714632

Transcript

Introduction to the show: ident: Robotics Radio. Generated commentary on the latest robotics and control papers.

Rosa: Today's paper: "Personalized and Robust Proactive Robot Assistance with Uncertainty-Guided LLM Reasoning".

Dev: The gist: GLOBE, a lightweight framework combining n-gram Markov models for temporal behavioral patterns with uncertainty-guided large language model (LLM) reasoning,

Rosa: First, who's behind it and why it matters.

Paper summary: Rosa: We're building on that idea of personalization now. The paper "Personalized and Robust Proactive Robot Assistance with Uncertainty-Guided LLM Reasoning" is proposing GLOBE as the framework to tackle this problem.

Dev: They combine n-gram Markov models for capturing those temporal behavioral patterns with uncertainty-guided Large Language Model reasoning, which is what makes the prediction smarter when things get messy two thousand six hundred six.

Taro: They're essentially saying that instead of just looking at the last few steps, you need semantic knowledge to guess what’s coming if the routine deviates from normal.

Rosa: Exactly. And they are doing this by using an observation phase where the robot records sequences and also associates each activity with a set of objects based on co-occurrence patterns observed during that time two thousand six hundred six.

Dev: During the inference phase, for activity prediction, they use an n-gram Markov model to estimate the probability of the next activity P(ˆat cact) = count(cact → at) / Σ a∈A count(cact → a) two thousand six hundred six.

Taro: And they handle object prediction by conditioning it on the predicted activity and recent object history within that activity, using P(ˆot cobj, at) = count(cobj → ot) / Σ o∈O(at) count(cobj → o) two thousand six hundred six.

Rosa: But where the LLM comes in is when the sequential model prediction confidence falls below a predefined threshold d, a hyperparameter two thousand six hundred six.

Dev: When that happens, the system queries the LLM to refine the prediction because it provides broader contextual knowledge and semantic relationships two thousand six hundred six.

Taro: This selective invocation of the LLM is key. They found that if you rely too much on this refinement, it actually reduces your performance score two thousand six hundred six.

Rosa: They even introduced HOMER-Noise to evaluate performance under realistic conditions, simulating structured disturbances like object movements caused by humans or toddlers two thousand six hundred six.

Dev: The results show GLOBE achieves competitive performance with state-of-the-art methods while improving robustness and computational efficiency across both clean and noisy settings two thousand six hundred six.

Taro: It also showed that GLOBE maintains stable object prediction performance even when user routines become less predictable, which is good for long term interactions two thousand six hundred six.

Rosa: So the main claim of this paper is that this lightweight framework can deliver strong performance while being smart enough to handle the messy reality of a home two thousand six hundred six.

Conclusion: Dev: To wrap up on "Personalized and Robust Proactive Robot Assistance with Uncertainty-Guided LLM Reasoning". The authors are proposing GLOBE as a framework combining n-gram Markov models with uncertainty-guided Large Language Model reasoning.

Rosa: They're showing that this approach can handle messy situations by selectively using the LLM when the model is unsure, which helps improve robustness and efficiency compared to older spatio-temporal models two thousand six hundred six.

Taro: It’s about making the system adaptive so it doesn't just stick to a fixed script but can use semantic reasoning when things get unpredictable two thousand six hundred six.

Dev: And they proved this by using the HOMER-Noise dataset to simulate disturbances like pet noise or toddler movement to test how well the system recovers in real scenarios two thousand six hundred six.

Rosa: So for anyone listening, this paper suggests that future robot assistance systems should focus on balancing strong prediction with computational speed and a smart fallback mechanism two thousand six hundred six.

Dev: The implication is we can move toward more robust home assistance where the AI doesn't just follow rules but adapts its predictions based on how uncertain it is about the user's actions two thousand six hundred six.

Rosa: That adaptability is key, and it’s exactly what we need to think about as we look at how these kinds of systems actually function when deployed in a busy kitchen or a living room.

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