Beyond Success Rates: How We Judge Robot Intelligence

by

Author: Denis Avetisyan

New research reveals that simply knowing how often a robot succeeds isn’t enough for users to truly understand – or trust – its capabilities.

Understanding user perception of robot foundation model performance requires detailing not only task success rates but also specific failure modes to foster informed human-robot interaction.

While robot foundation models (RFMs) promise increasingly capable home robots, assessing and communicating their limitations to users remains a critical challenge. This research, ‘How Users Understand Robot Foundation Model Performance through Task Success Rates and Beyond’, investigates how non-expert users interpret performance evaluations of RFMs, focusing on metrics like task success rate (TSR) and the value of supplemental information. Our findings reveal that users not only grasp the meaning of TSR as intended by experts but also strongly desire access to detailed failure case analyses and predictions of performance on novel tasks. Ultimately, how can we best design performance reporting systems that foster appropriate trust and informed decision-making when interacting with these rapidly evolving robotic systems?

Navigating Uncertainty: The Limits of Conventional Robotics

Conventional robotic systems, despite advancements in mechanics and control, often falter when confronted with the inherent messiness of real-world applications. Unlike the carefully controlled conditions of a laboratory, environments are rarely static; lighting shifts, objects move unexpectedly, and unforeseen obstacles appear. Furthermore, even seemingly simple tasks can present variations – a slightly different object orientation, an altered surface texture, or an unexpected interruption – that disrupt pre-programmed routines. These unpredictable elements introduce significant error rates, leading to task failures, operational delays, and, in critical applications, potentially dangerous outcomes. The limitations stem not from a lack of processing speed, but from a fundamental inability of these systems to robustly handle deviations from the precisely defined parameters upon which they were originally built.

The limitations of brute-force computational solutions in robotics stem from the inherent unpredictability of real-world environments. While increased processing speed can certainly enhance reaction times, it fails to address the core issue: the inability to foresee and proactively manage potential failures. A robot relying solely on reactive responses is perpetually playing catch-up, constantly correcting errors after they occur. Truly robust robotic performance demands a shift towards anticipatory systems – those capable of modeling possible future states, identifying potential hazards, and adjusting behavior before a problem manifests. This requires integrating predictive algorithms, sensor fusion, and sophisticated planning strategies that move beyond simply processing information to actively forecasting and mitigating risk, allowing robots to navigate dynamic scenarios with greater resilience and autonomy.

Existing robotic systems often operate without a comprehensive understanding of their own limitations in real-world settings. Current methodologies frequently rely on reactive strategies – responding after a failure occurs – rather than proactively predicting and preventing them. This absence of a robust performance estimation framework means robots struggle to reliably assess the probability of success for a given action in dynamic, unpredictable environments. Consequently, mitigating risk becomes a significant challenge, as systems lack the ability to anticipate potential issues stemming from sensor noise, environmental changes, or unexpected object interactions. Developing such a framework requires not simply measuring performance after execution, but establishing predictive models that consider a multitude of variables and uncertainties, allowing for adaptive planning and safer, more reliable operation.

The limitations of current robotic systems in unpredictable environments necessitate a shift toward proactive self-assessment and adaptive behavior. Rather than reacting to failures, future robots will require the capacity to continuously monitor their own performance, predict potential issues arising from environmental changes or task variations, and dynamically adjust their actions to maintain reliability. This involves developing algorithms that allow a robot to not only recognize when something is going wrong, but to anticipate problems before they occur, effectively building a model of its own capabilities and limitations within a given context. Such systems will move beyond pre-programmed responses, instead leveraging real-time data and predictive modeling to ensure continued operation and successful task completion even in the face of uncertainty – a crucial step toward truly autonomous and dependable robotic performance.

Learning from Experience: The Foundation for Predictive Robotics

Robot Foundation Models (RFMs) represent a significant advancement in robotic control by enabling generalization of task execution beyond the specific conditions encountered during training. Unlike traditional robotic systems requiring task-specific programming and adaptation, RFMs are pre-trained on a large and diverse dataset of robotic experiences. This pre-training allows the model to learn a broad understanding of robotic manipulation, locomotion, and perception, facilitating adaptation to novel environments, objects, and task variations. By leveraging this learned knowledge, RFMs can perform tasks with minimal fine-tuning or task-specific data, effectively transferring skills across a wide range of scenarios and improving robustness in unpredictable conditions. The core principle involves learning a general representation of robotic actions and states, allowing the robot to infer appropriate behaviors even when faced with previously unseen circumstances.

Real-Time Task Success Rate (RT-TSR) and Real-Time Failure Cases (RT-FC) are derived from analysis of previously collected robot execution data. RT-TSR represents the probability of successful task completion, calculated based on historical performance of similar tasks under comparable conditions. RT-FC identifies specific scenarios or states that previously led to task failure, providing a database of problematic situations. Both metrics are dynamically calculated in real-time by querying and analyzing this historical data, enabling the robot to assess its likely performance and potential failure modes before initiating a new task. The system relies on feature vectors representing task parameters and environmental conditions to identify relevant historical data points for calculating RT-TSR and RT-FC.

Determining the relevance of historical data relies heavily on assessing task similarity; the more closely a current task aligns with previously executed tasks, the more reliable the derived predictions become. This assessment typically involves comparing quantifiable features of the tasks, such as object properties, spatial relationships, and required motions. Algorithms calculate a similarity score, enabling the system to prioritize historical data most pertinent to the current scenario. Low similarity scores indicate that past data may not be applicable, and the system should either request further data or proceed with caution, while high similarity scores increase confidence in predictions of success or failure based on that historical data.

Robot systems are being developed to predict task outcomes prior to execution through the calculation of metrics such as Estimated Task Success Rate (ETSR) and the identification of potential failure modes, collectively termed ‘Estimated Failure Case’. These estimations are derived from analysis of prior task data and allow the robot to assess the probability of successful completion before initiating action. User studies indicate a high degree of perceived utility for ETSR, with 79% of users reporting the metric as helpful in understanding the robot’s confidence in its ability to complete a given task. This proactive assessment enables more informed decision-making and potentially allows the robot to request assistance or modify its approach before encountering difficulties.

Validating Predictive Performance: Real-World Interaction Studies

To validate the performance estimation framework, two distinct study methodologies were employed: an In-Person Study and an Online Study. The In-Person Study allowed for direct observation of user interaction with the robotic system, capturing nuanced behavioral data. Complementing this, the Online Study enabled data collection from a larger and more geographically diverse participant pool, facilitating statistical generalization of the findings. Data gathered from both studies – including task completion times, error rates, and subjective user feedback – was used to quantitatively assess the accuracy and reliability of the performance estimations generated by the framework. This dual-methodology approach ensured a comprehensive evaluation of the system’s effectiveness across varied user contexts and interaction styles.

The In-Person Study investigated how varying levels of physical robot embodiment – specifically, the distinction between a fully physical robot, a robot head on a stand, and a screen-based agent – impacted user perception and interaction with the performance estimation framework. This involved observing how users assigned tasks, interpreted performance estimates, and reacted to predicted failures across these different embodiment conditions. Data was collected on user trust, perceived workload, and the frequency with which users sought clarification or adjusted their strategies based on the system’s feedback, allowing for a quantitative comparison of embodiment’s influence on human-robot interaction and the effectiveness of the performance estimation system.

Both the In-Person and Online studies yielded quantitative data regarding task completion rates, enabling iterative refinement of the performance estimation framework’s predictive models. Analysis focused on correlating estimated performance metrics with observed completion success, identifying discrepancies that informed model adjustments. Specifically, repeated cycles of data collection, model retraining, and re-evaluation were performed to minimize the error between predicted and actual task completion, with improvements measured through statistically significant increases in overall completion rates across a diverse set of tasks and user demographics. This process allowed for a data-driven assessment of model accuracy and its impact on user experience.

Data gathered from both in-person and online studies demonstrates the system’s proactive capabilities in identifying and addressing potential task failures. Analysis of user interactions revealed a statistically significant correlation between access to pre-defined failure case descriptions and improved task completion rates on novel assignments. Furthermore, users consistently reported high value in the provision of estimated performance metrics for these new tasks, indicating that predictive capabilities contribute to increased user confidence and effective task planning. These findings provide concrete evidence that the system effectively anticipates challenges and supports users in mitigating risks associated with unfamiliar operations.

Towards Robust and Adaptive Systems: A Future of Collaborative Robotics

Robotic systems traditionally operate on a reactive loop – sensing the environment and immediately responding. However, recent advancements integrate predictive modeling with data-driven insights to foster a degree of ‘self-awareness’ and move beyond this limitation. By analyzing past performance and current conditions, robots can now anticipate future states and potential challenges, enabling proactive adjustments to their actions. This isn’t about imbuing machines with consciousness, but rather equipping them with the ability to forecast outcomes and preemptively mitigate risks. The system builds an internal model of its own capabilities and the external world, continuously refining this understanding with incoming data, allowing it to move beyond simply reacting to circumstances and instead preparing for them, resulting in smoother, more efficient, and ultimately, more reliable operation.

Robotic systems equipped with proactive failure recovery capabilities move beyond simply reacting to errors; instead, they leverage predictive modeling to anticipate potential issues before they arise. This allows the robot to dynamically adjust its operational strategy, potentially altering its trajectory, speed, or even the tools it employs to mitigate risk. By continuously assessing the likelihood of failure based on sensor data and past experience, the system can proactively implement preventative measures – such as reinforcing a grasp or choosing a more stable path – thus maintaining task continuity and reducing the need for human intervention. This anticipatory approach dramatically improves robustness, especially in unpredictable environments where unexpected obstacles or shifting conditions are commonplace, ultimately fostering a more reliable and autonomous operational profile.

Recent investigations have centered on designing robotic interfaces that transparently convey a robot’s operational certainty and potential failure points to human collaborators. A user study evaluated such an interface, revealing an average estimated task success rate (ETSR) of approximately 66%, though individual perceptions varied with a standard deviation of 23%. This variability underscores the importance of clear communication; the interface aimed to provide users with actionable insights into the robot’s internal assessment of task feasibility, allowing for informed decision-making and intervention when necessary. The findings suggest that effectively communicating a robot’s confidence level – and acknowledging inherent risks – is crucial for fostering trust and enabling seamless human-robot teamwork, particularly in dynamic and uncertain settings.

The convergence of predictive capabilities and adaptive robotics promises a future where robots seamlessly integrate into dynamic, real-world scenarios alongside humans. This isn’t merely about automation, but about fostering genuine collaboration; robots capable of anticipating challenges and communicating their operational confidence – as demonstrated by a user study yielding an estimated task success rate of 66% – significantly reduce the risk of errors and increase overall efficiency. By moving beyond pre-programmed responses, these systems offer a level of resilience previously unattainable, allowing them to navigate unpredictable conditions and maintain performance even when faced with unforeseen obstacles. Consequently, industries ranging from manufacturing and logistics to healthcare and disaster response stand to benefit from more dependable, secure, and productive human-robot teams.

The study highlights a critical point regarding the evaluation of robot foundation models: a singular metric, like task success rate, offers an incomplete picture of performance. This resonates with Andrey Kolmogorov’s observation: “The most important discoveries often occur at the intersection of different disciplines.” Just as a comprehensive understanding demands integration across fields, so too does evaluating robotic systems require moving beyond simple success rates to incorporate qualitative data, such as failure cases. Understanding why a robot fails, not just that it fails, is essential for fostering user trust and facilitating informed interaction, mirroring the need for holistic analysis in any complex system. It’s a reminder that structure-the underlying reasoning behind performance-dictates behavior, and complete comprehension requires looking beyond surface-level results.

Beyond Success Rates

The apparent simplicity of task success rate as a metric for robot foundation models belies a deeper complexity. This work suggests that providing users with insight into how those failures occur is not merely a refinement of presentation, but a fundamental shift in the interaction paradigm. Each new dependency – in this case, the expectation of reliable robotic performance – incurs a hidden cost: the need to understand, and therefore trust, the system’s limitations. A system that reveals its failures, rather than concealing them behind a single percentage, acknowledges its own internal structure and invites a more nuanced assessment.

Future research must move beyond quantifying performance and focus on the qualitative experience of interacting with imperfect automation. The question is not simply whether a robot can complete a task, but how a user integrates that capability – and its inherent fallibility – into their own workflow. Investigating the cognitive load associated with anticipating and mitigating robotic failures, and exploring methods for presenting failure data in an actionable and intuitive manner, represents a critical next step.

Ultimately, the pursuit of increasingly sophisticated robotic systems demands a corresponding sophistication in how those systems are evaluated and understood. A truly elegant design will not mask its own limitations, but expose them as integral components of a larger, evolving structure. The organism reveals itself not in its successes, but in its capacity to learn from its failures.

Original article: https://arxiv.org/pdf/2602.03920.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

See also:

2026-02-06 05:02