Robots steer human choices to reveal hidden goals in teamwork and competition

A team of researchers at Yale University, the Massachusetts Institute of Technology, and the University of Colorado Boulder has unveiled a new strategy that allows robots to do something far more subtle than simply follow instructions: actively shape human behavior so that people reveal their goals faster and more clearly. Rather than waiting passively for a human partner’s intentions to become obvious through observation alone, the robot deliberately nudges the human toward moments in a task where their next move betrays what they are really trying to accomplish. The work, published in the journal Autonomous Robots, demonstrates that this approach produces faster and more accurate goal inference in both cooperative cooking tasks and competitive hide-and-seek games, in simulation and on physical robot platforms.

The central insight behind the research is a concept the authors call Critical Decision Points, or CDPs. These are specific states in a shared task where two or more competing human strategies would prescribe clearly different next actions. Imagine a kitchen robot collaborating with a person on a recipe: if two different dishes the person might be preparing require identical actions up to a certain moment, watching the person gives the robot no new information. But if the robot guides the interaction so that the person reaches a junction where making soup demands reaching for a pot while making a salad demands reaching for a cutting board, the very next action unambiguously discloses the goal. CDPs are precisely those junctions, and they maximally reveal intent because the divergent strategies force a distinguishing choice.

To turn this intuition into a computable method, the researchers formalized CDPs using a goal-conditioned policy divergence measure. In plain terms, the team evaluated, for any given state of the world, how differently an optimal policy would behave depending on which goal the human is pursuing. High divergence means the state is highly informative: small differences in behavior at that state separate the candidate goals cleanly. Low divergence means the state tells the robot almost nothing, because all plausible goals call for the same action. By quantifying this divergence, the robot can rank future states by how much information they would generate about the human’s hidden objective.

Measuring informativeness, however, is only half the problem. The robot also has to accomplish the actual task, whether that means assembling ingredients in a kitchen or chasing down an opponent in a game. The team therefore embedded the CDP framework inside a Receding-Horizon Planner, a control architecture rooted in model predictive control that repeatedly looks ahead over a short window of future actions, optimizes a cost function, executes the first action, and then replans. The cost function balances two competing objectives: task progress, meaning the robot keeps moving the joint activity toward completion or toward winning, and information gain, meaning the robot steers toward states that maximize policy divergence and therefore reveal the human’s goal. The receding-horizon structure keeps the planning computationally tractable, because the robot never has to search the full branching tree of future interactions; it only reasons a few steps ahead at a time while still accounting for how its own actions will shape what the human does next.

What makes the work especially notable is that the same unified framework operates in two dramatically different social regimes. In the collaborative setting, the robot and human share a fully observable cooking task, and both parties benefit from smooth coordination. Here, steering the human toward CDPs serves partnership quality: the earlier the robot knows whether its partner is preparing one dish or another, the earlier it can hand over the right utensil, clear the correct counter space, or divide labor sensibly. Faster and more accurate goal inference translates directly into more fluent teamwork, reduced awkward pauses, and fewer mistaken anticipations of what the human wants next.

In the competitive setting, the tables turn entirely. The two agents play a partially observable hide-and-seek game in which the human’s goal is hidden from the robot, and revealing it works to the robot’s advantage rather than the human’s. Even here, the CDP-driven planner proved effective: by maneuvering the human into states where the seeker’s and hider’s strategies prescribe different moves, the robot extracted decisive evidence about the opponent’s hidden objective. The authors frame this in the tradition of opponent modeling, in which an agent builds and refines a predictive model of a rival’s intentions from observed behavior. Provoking opponents into informative situations has precedent in the game-playing literature, but this study is among the first to unify adversarial information gathering and cooperative assistance under a single planning objective.

Evaluation was deliberately thorough. The researchers tested their method in both domains in simulation and then transferred the approach to real robots interacting with actual human participants, a transition that notoriously breaks many planning algorithms due to perception noise, motion constraints, and unpredictable human behavior. Across both simulated and physical trials, the CDP-based planner inferred human goals more accurately and, crucially, earlier than baseline strategies that either pursued the task greedily or gathered information without shaping the human. The earlier a robot can commit to a confident estimate of a partner’s goal, the more useful actions it can take on that partner’s behalf, which is why the timing improvement matters as much as the accuracy improvement.

The implications stretch well beyond kitchens and games. Assistive robots in homes, factories, and hospitals constantly face the problem of deciphering what a person is trying to do, often from ambiguous and fragmentary evidence. A wheelchair-mounted manipulator that can gently reposition a shared workspace so that a user’s next movement disambiguates their intent could provide safer and more responsive support. Warehouse cobots could coordinate with human pickers more fluidly. Even autonomous vehicles, which increasingly reason about the intentions of human drivers, could benefit from planners that recognize which traffic situations are most informative about another driver’s route. The study builds on a growing body of research showing that influence is a two-way street: humans adapt to robots, and robots that account for this adaptation, rather than treating human behavior as fixed background noise, plan fundamentally better.

At the same time, the work raises questions the authors themselves situate within a broader research conversation about robots that influence humans. Steering a person’s choices, even mildly and for legitimate informative purposes, touches on autonomy and transparency, and the competitive results in particular show that the same mathematics that helps a helpful kitchen assistant can empower an adversarial agent. The researchers report no competing interests and ground their contribution firmly in the engineering goal of faster mutual understanding. As robots move from controlled labs into shared human spaces, frameworks like CDPs suggest a future in which machine teammates do not merely watch and guess, but intelligently arrange the world so that human intentions become legible — a capability that promises smoother collaboration, sharper competition, and a clearer science of how people and machines can read each other’s minds in real time.

The study also connects to a longer intellectual lineage in machine learning and control. The idea of learning a reward function or objective from observed behavior traces back to inverse reinforcement learning, which showed that an observer can recover what an agent is optimizing by watching its actions. The present work inverts that logic: instead of passively accumulating observations, the robot actively engineers the observation conditions, choosing its own actions so that the human’s behavior becomes maximally diagnostic. This places the method in the emerging field of influence-aware planning, where an agent treats its partner’s future decisions as part of the environment it can shape rather than as an exogenous process it must simply predict.

Methodologically, the reliance on a receding-horizon architecture reflects decades of practice in model predictive control, a technique originally developed in industrial process control and now widespread in robotics because it handles constraints and replans gracefully under uncertainty. Adapting this mature machinery to a social objective — trading off task progress against epistemic value — is a notable engineering contribution, since classical cost functions rarely encode anything like information gain about a partner’s intent.

The dual-domain evaluation also strengthens the claim of generality. Many goal-recognition systems are validated in a single task, leaving open whether the underlying measure of informativeness is task-specific. By demonstrating that the same policy-divergence criterion improves inference in a fully observable cooperative setting and a partially observable adversarial one, the authors provide evidence that Critical Decision Points are a domain-independent structural property of sequential decision problems, not an artifact of a particular scenario. The consistency of results across simulation and physical hardware further suggests the approach is robust to the perception noise and behavioral variability that typically degrade deployed interactive planners.

Subject of Research: Robot planning strategies that influence human behavior to accelerate goal inference during collaboration and competition.

Article Title: Robots influencing humans to reveal their goals during collaboration and competition

Article References: Ghose, D., Gitelson, O., Lewkowicz, M., Brawer, J., Roncone, A., Vazquez, M., & Scassellati, B. (2026). Robots influencing humans to reveal their goals during collaboration and competition. Autonomous Robots, 50(3), Article 38. https://doi.org/10.1007/s10514-026-10267-2

Image Credits: AI Generated

DOI: 10.1007/s10514-026-10267-2

Keywords: human-robot interaction, goal inference, critical decision points, receding horizon planning, social robotics, opponent modeling, human-robot collaboration, game theory, artificial intelligence, planning, shared autonomy, Robots