Reading Your Mind: Robots That Understand What You Want

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Author: Denis Avetisyan

New research explores how robots can combine observations of human actions with natural language to infer intentions and collaborate more effectively on complex, open-ended tasks.

A collaborative cooking scenario demonstrates how a robot navigates ambiguous human intent-initially responding to a request for “quick Italian dinner” by prioritizing pasta, then clarifying sauce preference with a targeted question (“red or white?”) to resolve uncertainty and successfully complete the dish, highlighting a system where the robot doesn’t <i>execute</i> a plan, but <i>grows</i> understanding through iterative interaction and clarification.
A collaborative cooking scenario demonstrates how a robot navigates ambiguous human intent-initially responding to a request for “quick Italian dinner” by prioritizing pasta, then clarifying sauce preference with a targeted question (“red or white?”) to resolve uncertainty and successfully complete the dish, highlighting a system where the robot doesn’t execute a plan, but grows understanding through iterative interaction and clarification.

This work introduces BALI, a system enabling bidirectional reasoning and clarifying questions to improve goal inference in human-robot collaboration.

Effective human-robot collaboration demands robots navigate ambiguous goals, yet current approaches typically rely on predefined tasks or limited input modalities. This paper, ‘Open-Ended Goal Inference through Actions and Language for Human-Robot Collaboration’, introduces BALI, a novel method that integrates observed human actions with natural language to infer goals in dynamic, open-ended scenarios. By employing bidirectional reasoning and strategically requesting clarification, BALI achieves more stable goal predictions and reduces errors in collaborative tasks. Could this approach unlock more intuitive and robust human-robot partnerships in increasingly complex real-world environments?

The Illusion of Shared Intent

Many collaborative endeavors between humans and robots are complicated by the prevalence of unstated goals. Unlike a robot programmed with explicitly defined objectives, a human partner frequently operates with implicit understandings and assumptions about the task at hand – expectations they may not articulate. This creates a significant challenge for robotic systems, which struggle to interpret ambiguous situations and anticipate a collaborator’s unvoiced intentions. The robot must then navigate a ‘goal space’ defined not only by observable actions, but also by these hidden expectations, potentially leading to miscommunication, inefficiency, and ultimately, a breakdown in collaborative effort if the robot fails to bridge this gap between stated and unstated objectives.

Current methods for enabling robots to understand human intentions frequently falter when faced with the subtleties of real-world collaboration. These approaches often rely on pre-programmed responses to specific, observable actions, proving inadequate when humans leave goals unstated or communicate them implicitly. A robot equipped with such limited perception may misinterpret ambiguous cues – a quick glance towards a tool, a partially completed step – leading to inefficiencies or even errors in shared tasks. The difficulty lies in extracting comprehensive understanding from sparse data; humans routinely leverage extensive background knowledge and contextual awareness to fill in gaps, a capability that remains a significant hurdle for robotic systems striving for truly intuitive interaction. Consequently, robots struggle to predict future actions or proactively offer assistance, hindering their ability to function as effective collaborative partners.

Effectively operating within an undefined task environment demands that a robotic system move beyond simply recognizing what a human is doing, to understanding why they are doing it. Current approaches often falter because they rely on pre-programmed expectations or limited behavioral cues; however, truly robust interpretation necessitates a synthesis of observational data – analyzing physical actions, gaze direction, and even subtle physiological signals – with natural language processing. By cross-referencing spoken instructions, clarifications, and even seemingly casual remarks with observed behaviors, a system can build a probabilistic model of the human’s underlying intent. This integrated approach allows the robot to not only anticipate future actions but also to proactively seek clarification when ambiguity arises, ultimately enabling a more fluid and collaborative partnership in tasks with loosely defined objectives.

Effective collaboration between humans and robots hinges on a robot’s ability to discern unstated goals – those implicit understandings that naturally arise during shared activities. Without this capacity, robotic partners remain limited by literal interpretations, hindering their ability to anticipate needs or offer proactive assistance. Successfully inferring these goals allows robots to move beyond simple instruction-following and instead participate as true collaborators, adapting to nuanced situations and completing tasks with a level of fluidity that mirrors human teamwork. This proactive understanding is not merely about efficiency; it’s about building trust and establishing a natural, intuitive interaction where the robot feels less like a tool and more like a partner, ultimately fostering more effective and satisfying shared experiences.

BALI predicts goals by integrating human preferences and actions to infer plausible objectives, requesting clarification when uncertain, and efficiently planning actions via a cost function that prioritizes alignment with inferred goals.
BALI predicts goals by integrating human preferences and actions to infer plausible objectives, requesting clarification when uncertain, and efficiently planning actions via a cost function that prioritizes alignment with inferred goals.

Action and Language: A Bidirectional Bridge

Bidirectional Action Language Inference (BALI) represents a departure from traditional human goal prediction methods by integrating both action observation and natural language processing. Existing systems often rely solely on analyzing physical actions to infer intent; BALI, however, operates on the principle that language provides crucial contextual information that clarifies ambiguous actions and constrains the space of possible goals. This bidirectional approach allows the system to not only interpret language in the context of observed actions, but also to use language to actively seek clarification and refine its understanding of the human’s objective, ultimately improving prediction accuracy and enabling more effective human-robot collaboration.

Receding Horizon Planning (RHP) within the BALI framework operates by iteratively predicting future states and evaluating the plausibility of potential goal trajectories. RHP functions by constructing a limited-horizon plan – a sequence of actions – based on the current observation and predicted outcomes. This plan is then repeatedly re-evaluated and re-planned as new observations become available, effectively ‘receding’ the horizon of prediction. Plausibility is determined by assessing the likelihood of each trajectory given the observed actions, language inputs, and prior knowledge, utilizing probabilistic models to quantify the feasibility of reaching a specific goal state. The system maintains a distribution over possible goals, updating this distribution with each planning iteration based on the evaluated plausibility of different trajectories.

Attractor Fields, within the BALI framework, function as a numerical representation of the relationship between observed actions and potential goals. These fields assign a scalar value indicating the degree to which a given action supports or aligns with achieving a specific goal; higher values signify stronger relevance. The computation of these fields relies on semantic similarities derived from knowledge graphs, allowing the system to assess how effectively an action moves the agent closer to a hypothesized intention. During goal inference, the system utilizes these quantified relevance scores to prioritize exploration of likely goal trajectories, effectively weighting the search process and accelerating convergence towards the most plausible intentions based on observed behavior.

The Bidirectional Action Language Inference (BALI) system incorporates a clarifying question strategy to actively reduce uncertainty in goal prediction. This involves the robot formulating and posing questions to gather additional information about the user’s intentions. When integrated with knowledge graphs and probabilistic planning methods, this questioning approach has demonstrated a top-1 goal prediction accuracy of up to 95.04%. The system’s ability to strategically request relevant information allows it to disambiguate potential goals and refine its understanding of the user’s objective, significantly improving prediction performance compared to passive observation alone.

Large Language Models as Evaluative Oracles

Within the BALI framework, Large Language Models (LLMs) function as evaluative components, termed ‘judges’, responsible for quantifying the alignment between proposed actions and established goals. This integration involves providing the LLM with both the current dialogue state and a potential action, prompting it to assess the action’s compatibility with achieving the defined goal. The LLM outputs a score representing this compatibility, which is then used to refine action selection and improve overall task completion. This ‘judge’ function moves beyond simple action prediction by providing a continuous, nuanced evaluation of action appropriateness within the conversational context.

The implementation of an LLM as a ‘judge’ within BALI moves beyond the limitations of standard LLM functionalities by introducing a dedicated reasoning component. This approach allows for the evaluation of actions not simply based on direct instruction following, but on their compatibility with overarching goals and contextual understanding. Unlike basic LLMs which often operate on surface-level patterns, LLM-as-a-Judge facilitates a more nuanced assessment of intent, considering the implications of each action within the broader dialogue state. This extends reasoning capabilities to include assessing the appropriateness of an action given the current context and desired outcome, rather than merely its grammatical correctness or semantic plausibility.

Dialogue State Tracking (DST) in BALI functions by maintaining a condensed record of the conversational exchange, capturing relevant entities, intents, and slots. This history serves as crucial context for the LLM when evaluating the appropriateness of proposed actions and interpreting user language. The tracked dialogue state is not a verbatim transcript; instead, it’s a structured representation focusing on the core elements necessary for goal-oriented reasoning. By providing this concise contextual history, the LLM can accurately assess whether a given action aligns with the user’s previously stated objectives and the current conversational turn, thereby improving the overall coherence and success rate of the interaction.

BALI, utilizing large language models, demonstrated a 78.90% top-1 accuracy in predicting user goals, representing a substantial improvement over existing baseline methods. Further refinement of the system through integration with knowledge graphs and probabilistic planning algorithms resulted in a significant reduction of errors, achieving a mistake rate as low as 0.12. This indicates that combining LLMs with structured knowledge and planning techniques substantially enhances the reliability and precision of goal prediction within the BALI framework.

The Architecture of Anticipation

The GOOD method establishes a robust system for predicting human intentions by leveraging the principles of Bayesian inference. Rather than fixating on a single, most likely goal, it maintains a probability distribution encompassing all potential objectives, weighted by their plausibility given observed actions. As new evidence – such as a glance, a reach, or a spoken request – becomes available, this distribution is dynamically updated using Bayes’ theorem. This continual refinement allows the system to move beyond simple prediction, effectively quantifying uncertainty and adapting to evolving circumstances. By representing goals as probabilistic distributions, GOOD facilitates more nuanced and reliable anticipation of human needs, enabling proactive assistance even in the face of ambiguous or unexpected behavior. The method effectively models belief states about goals as $P(goal|observations)$, continuously updated with incoming data to refine its understanding of the human’s intent.

BALI leverages Bayesian reasoning, specifically through the GOOD method, to navigate the inherent unpredictability of human actions. Instead of rigidly predicting a single outcome, the system maintains a probability distribution over various possible goals, constantly refining these probabilities as new observations become available. This probabilistic approach allows BALI to gracefully handle deviations from expected behavior; unexpected actions aren’t treated as failures, but rather as evidence that shifts the probability towards alternative, yet still plausible, goals. The system effectively asks “what is the likelihood of this action given the range of possible goals?”, and updates its internal model accordingly. By quantifying uncertainty and adapting to new information, BALI demonstrates a remarkable capacity to anticipate human needs even when faced with ambiguous or surprising inputs, fostering more robust and intuitive human-robot interaction.

BALI’s capacity for proactive assistance stems from its continuous refinement of beliefs about a human’s intentions. Rather than reacting to explicitly stated needs, the system maintains an internal probabilistic model that evolves with each observed action. This dynamic belief updating allows BALI to move beyond simple prediction and towards anticipation; by constantly assessing the likelihood of various goals, the system can preemptively offer relevant tools or information. This isn’t merely about guessing what comes next, but about building a nuanced understanding of the human’s overall objective, enabling BALI to provide support before it is even requested and fostering a more seamless, intuitive collaborative experience.

Real-world evaluations of BALI demonstrate its capacity for practical application, achieving inference speeds of 12 to 14 seconds per timestep when utilizing an NVIDIA RTX 4090 GPU. This computational efficiency is critical, enabling BALI to function effectively within the timeframe required for genuine interactive scenarios. Consequently, this responsiveness translated directly into measurable gains in task completion rates and a substantial increase in user satisfaction, suggesting the probabilistic inference framework not only allows for robust goal prediction but also fosters a more intuitive and helpful human-robot collaboration experience.

The pursuit of truly collaborative systems, as demonstrated by BALI’s approach to open-ended goal inference, often feels less like construction and more like tending a garden. The system attempts to bridge the gap between human intention and robotic action, recognizing that goals are rarely explicitly stated but rather revealed through a dance of language and observed behavior. As Brian Kernighan observed, “Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it.” This echoes the inherent complexity of anticipating human needs; the more elegantly a system attempts to infer intent, the more fragile it becomes when faced with the unpredictable nature of open-ended tasks. The receding horizon planning, while a technical achievement, merely addresses a symptom of a deeper truth: architecture isn’t structure – it’s a compromise frozen in time, perpetually out of sync with the evolving ecosystem it seeks to serve.

The Horizon Recedes

This work, focused on inferring intent through observation and dialogue, merely postpones the inevitable confrontation with ambiguity. BALI, and systems like it, build clarifying questions as a defense against the unknown, a tacit admission that complete understanding is a fiction. Each successful inference isn’t a step toward resolution, but a narrowing of the space where failure will eventually manifest. The true test won’t be handling explicit goals, but gracefully degrading when the human’s purpose remains fundamentally opaque, a situation this architecture addresses only through more questioning-a temporary stay of execution, not a cure.

Future efforts will undoubtedly refine the linguistic bridge, attempting to map nuance onto action. However, the real challenge lies not in richer semantics, but in accepting the inherent incompleteness of the model. A robust system won’t strive for perfect inference, but for the ability to detect its own epistemic limits-to know when it is operating on a phantom goal. The current approach assumes goals are discrete and ultimately knowable; the next iteration must confront the possibility that some human actions are, at their core, exploratory-lacking even a coherent internal representation.

The pursuit of bidirectional reasoning is a comforting illusion. It implies symmetry, a shared cognitive ground. Yet, the system remains fundamentally reactive, interpreting signals from a source it can never fully comprehend. The horizon of collaboration will always recede, revealing not clarity, but deeper layers of uncertainty. The longevity of such systems won’t be measured in accuracy, but in the elegance of their inevitable failures.

Original article: https://arxiv.org/pdf/2512.04453.pdf

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

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2025-12-05 07:00