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

arXiv:2606.08458 · cs.RO · Submitted 2026-06-07 · Read on arXiv

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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.

Concordia University

cs.RO

Submitted: 2026-06-07

Updated: 2026-06-07

Comments: Accepted to the 2026 IEEE 35th International Conference on Robot and Human Interactive Communication (RO-MAN)

DOI: 10.1109/RO-MAN72286.2026.11714632

Code: https://github.com/PaInt-Lab/GLOBE

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 76/100

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

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

Robustness — 22× in this paper · explained in 5 episodes
Consistency — 9× in this paper · explained in 3 episodes
F1 Score — 8× in this paper · explained in 3 episodes
Adaptability — 4× in this paper · explained in 1 episode
Ablation — 3× in this paper · explained in 1 episode
Ablation Study — 3× in this paper · explained in 3 episodes
Large Language Model (LLM) — 3× in this paper · explained in 3 episodes
Accuracy — 2× in this paper · explained in 1 episode
Adaptive Reasoning — 2× in this paper · explained in 2 episodes
Continual Learning — 2× in this paper · explained in 2 episodes
Personalization — 2× in this paper · explained in 2 episodes
Planner — 2× in this paper · explained in 2 episodes
Benchmark — 1× in this paper · explained in 2 episodes
Evaluation Framework — 1× in this paper · explained in 2 episodes
Generalization — 1× in this paper · explained in 13 episodes
Hybrid Approach — 1× in this paper · explained in 4 episodes
Large Language Models (LLMs) — 1× in this paper · explained in 8 episodes
Semantic Understanding — 1× in this paper · explained in 2 episodes
Transformer — 1× in this paper · explained in 2 episodes
Transformers — 1× in this paper · explained in 2 episodes

Summary

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 with state-of-the-art methods while improving robustness and computational efficiency across both clean and noisy settings.

Problem Formulation

The paper addresses the problem of personalized longitudinal proactive robot assistance for household tasks by considering a household environment consisting of a set of objects O = The state of the environment at time t is represented as Gt = The robot receives sequential observations of the environment state Gt and user activities at ∈ A, which it uses to learn a predictive model of the user’s behavior<ref:2606.08458#pg2> In this paper, we address personalized longitudinal proactive robot assistance for household tasks<ref:2606.08458#pg2>.

The formulation of proactive assistance is described as a two-phase process consisting of an observation (training) phase and an assistance (inference) phase<ref:2606.08458#pg2>. During the observation phase, the robot records sequential observations of the environment state Gt and user activities at ∈ A, which it uses to learn a predictive model of the user’s behavior<ref:2606.08458#pg2>. To reduce ambiguity in object prediction, the system also associates each activity with a set of objects based on co-occurrence patterns observed during this phase<ref:2606.08458#pg2>.

Sequential Prediction Framework

The framework operates in a perception-driven loop, where the model first predicts the next activity, based on knowledge gained in the observation (training) phase, followed by object prediction conditioned on the predicted activity<ref:2606.08458#pg2>.

Activity Prediction: During inference, an n-gram Markov model is used to estimate the probability of the next activity P(ˆat cact) = count(cact → at) / Σ a∈A count(cact → a)<ref:2606.08458#pg2>. The model leverages the longest observed activity sequence to predict the next activity, and if that sequence was not encountered during training, progressively shorter recent sequences are used to estimate the transition probability<ref:2606.08458#pg2>.
Object Prediction: Object prediction is performed after predicting the activity at, conditioned on the predicted activity at and recent object history within that activity<ref:2606.08458#pg2>. The probability of the next object is estimated as P(ˆot cobj, at) = count(cobj → ot) / Σ o∈O(at) count(cobj → o)<ref:2606.08458#pg2>. Object prediction is restricted to the set O(at), associated with the activity at, ensuring consistency with the current activity context<ref:2606.08458#pg2>.

LLM-based Adaptive Reasoning

To address limitations of sequential models when context is sparse or ambiguous, an LLM is incorporated as a semantic reasoning module that is selectively invoked under uncertainty<ref:2606.08458#pg2>. When the sequential model prediction confidence falls below a predefined threshold (d, a hyperparameter), the system queries the LLM to refine the prediction<ref:2606.08458#pg2>. The LLM is provided with recent activity, object sequence, and top-k predictions from the n-gram model to produce a refined prediction that accounts for broader contextual knowledge and semantic relationships<ref:2606.08458#pg2>.

HOMER-Noise

HOMER-Noise is an extension of HOMER+ designed to capture environmental disturbances in simulated households<ref:2606.08458#pg2>. It augments HOMER+ with object-location disturbances modeled in a semantically grounded manner, including Person noise, Pet noise, and Toddler noise<ref:2606.08458#pg2>. These disturbances are generated by prompting an LLM with the current activity, object sequence, and environment context to simulate plausible object movements under each noise condition<ref:2606.08458#pg2>. This results in a dataset that preserves the activity labels, temporal structure, and object information of HOMER+ while introducing controlled disturbances into the environment<ref:2606.08458#pg2>.

Experimental Results and Analysis

GLOBE achieves a mean F1-score of 0.73 on HOMER+ and 0.74 with SLaTe-PRO, closely matching the performance of SLaTe-PRO (0.74)<ref:2606.08458#pg2>. For activity prediction, GLOBE achieves the highest performance with an F1-score of 0.80, slightly outperforming both SLaTe-PRO (0.78) and the n-gram baseline (0.79)<ref:2606.08458#pg2>. For object prediction, GLOBE achieves an F1-score of 0.65, improving over the n-gram baseline (0.64) while remaining competitive with SLaTe-PRO (0.69)<ref:2606.08458#pg2>. GLOBE demonstrates more stable and robust performance across different noise conditions, particularly in object prediction under challenging disturbances<ref:2606.08458#pg2>.

Computational Efficiency and Robustness

GLOBE requires only 8.33 seconds for training, which is significantly lower than SLaTe-PRO (11,415 seconds) and comparable to the n-gram baseline<ref:2606.08458#pg2>. During inference, GLOBE incurs higher latency (192.49 seconds) compared to both SLaTe-PRO (93 seconds) and the n-gram baseline (0.86 seconds)<ref:2606.08458#pg2>. This increase is primarily due to the selective use of LLM-based reasoning, where external API calls introduce additional overhead<ref:2606.08458#pg2>.

The framework demonstrates improved performance under structured noise, with GLOBE consistently outperforming SLaTe-PRO across all noise types<ref:2606.08458#pg2>. Furthermore, GLOBE maintains stable object prediction performance even when user routines become less predictable, highlighting its robustness to both behavioral inconsistency and environmental noise<ref:2606.08458#pg2>.

Ablation Study

The ablation study shows that the framework is not highly sensitive to LLM behavior; however, overly conservative reliance on LLM-based refinement reduces the mean F1-score<ref:2606.08458#pg2>. The best results are consistently obtained with d = 0.5, while a higher threshold of 0.75 leads to a slight degradation, and a lower threshold of 0.25 results in a more pronounced drop in performance<ref:2606.08458#pg2>.

Robot Validation

The proposed framework is validated using a representative scene with a Stretch 3 robot equipped with an RGB camera for object identification<ref:2606.08458#pg2>. The robot observes that the user typically drinks coffee while working at a computer around 6 a.m.<ref:2606.08458#pg2>. In the assistance phase, the model predicts both the next activity and the corresponding objects likely to be used<ref:2606.08458#pg2>. Before acting, if prediction confidence is low, the LLM is invoked to refine the estimate<ref:2606.08458#pg2>. The robot continues to observe user behavior and incrementally updates the model with newly observed activity–object pairs<ref:2606.08458#pg2>.

Conclusion

GLOBE provides a lightweight framework that combines n-gram Markov models for capturing temporal behavioral patterns with LLM-based reasoning to handle uncertainty<ref:2606.08458#pg2>. The approach achieves competitive performance with SOTA methods while providing improved robustness and computational efficiency during model training<ref:2606.08458#pg2>. GLOBE demonstrates more stable and robust performance across different noise conditions, particularly in object prediction under challenging disturbances<ref:2606.08458#pg2>. The framework is designed to support personalized longitudinal proactive robot assistance in dynamic household environments<ref:2606.08458#pg2>.

References

[1] H.-M. Gross, S. Mueller, C. Schroeter, M. Volkhardt, A. Scheidig, K. Debes, K. Richter, and N Doering “Robot companion for domestic health assistance: Implementation test and case study under everyday conditions in private apartments” in IROS Hamburg Germany: IEEE Sept 2015 pp 5992–5999<ref:2606.08458#pg2>.

[3] M Patel, A Prakash, and S Chernova “Predicting routine object usage for proactive robot assistance” in CoRL 2023<ref:2606.08458#pg2>.

Improvements for AI systems

Bold Header: Selective LLM Reasoning Integration

The system can selectively invoke LLM reasoning when the model confidence is low to refine predictions, improving robustness to noise without incurring constant latency or cost from a full LLM invocation.

Bold Header: Hybrid Sequential Prediction Engine

The framework combines n-gram Markov models for capturing temporal behavioral patterns with uncertainty-guided large language model (LLM) reasoning, enabling efficient sequential prediction while selectively leveraging LLM reasoning when required.

Bold Header: Uncertainty-Guided Object Prediction

The system can achieve competitive performance with state-of-the art methods while improving robustness and computational efficiency across both clean and noisy settings by using the LLM to utilize its semantic reasoning ability for reliable object prediction when sequential model confidence is low.

Bold Header: Structured Noise Simulation

The introduction of HOMER-Noise allows the system to be trained on data that simulates structured disturbances such as object movements caused by humans, pets, and toddlers, thereby improving performance under realistic environmental variability.

Bold Header: Enhanced Robustness to Behavioral Inconsistency

The model demonstrates more stable and robust performance across different noise conditions, specifically maintaining performance in object prediction even when user routines become less predictable or inconsistent.

Abstract

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.