I've Changed My Mind: Robots Adapting to Changing Human Goals during Collaboration Abstract --For effective human-robot collaboration, a robot must align its actions with human goals, even as they change mid-task. Prior approaches often assume fixed goals, reducing goal prediction to a one-time inference. However, in real-world scenarios, humans frequently shift goals, making it challenging for robots to adapt without explicit communication. We propose a method for detecting goal changes by tracking multiple candidate action sequences and verifying their plausibility against a policy bank. Upon detecting a change, the robot refines its belief in relevant past actions and constructs Receding Horizon Planning (RHP) trees to actively select actions that assist the human while encouraging Differentiating Actions to reveal their updated goal. We evaluate our approach in a collaborative cooking environment with up to 30 unique recipes and compare it to three comparable human goal prediction algorithms. Our method outperforms all baselines, quickly converging to the correct goal after a switch, reducing task completion time and improving collaboration efficiency. N real-world scenarios, humans often change their goals in response to evolving circumstances, new information, or spontaneous decisions. Previous work often addresses changing human goals by relying on explicit communication [1], [2], [3]. While effective, relying on communication assumes humans can and will communicate with the robot, which is often impractical due to physical, situational, or cognitive constraints [4], [5], [6], [7], [8].

Weaknesses: My main concerns for the work are about specific assumptions made regarding the agent's planning algorithm and how close the effectiveness of the goal recognition system is tied to having access to the specific planning algorithm and parameters used by the agent generating the observations. I would have liked to see experimental results that at least shows some level of robustness of the system towards mismatch between the planning algorithm used by the goal recognition system and the method used to generate the observations for the study. Below I have provided a more detailed discussion of my main concerns Specific Algorithm Used: The paper makes some specific assumptions on the kind of algorithm that could be used to simulate the bounded decision making. I see no reason to believe that this is general enough to capture behavior of any arbitrary resource bounded decision-maker (for example consider one that is quite similar to the one discussed, but is also memory bounded and can only hold limited possible nodes in its open list) or that this is in anyways similar to how a human would make such decisions (which is important if the primary goal is to be able to predict human goals). While the paper notes that people use heuristics as well, those may be quite different from the ones that are popular in planning literature.

Existing game AI research mainly focuses on enhancing agents' abilities to win games, but this does not inherently make humans have a better experience when collaborating with these agents. For example, agents may dominate the collaboration and exhibit unintended or detrimental behaviors, leading to poor experiences for their human partners. In other words, most game AI agents are modeled in a "self-centered" manner. In this paper, we propose a "human-centered" modeling scheme for collaborative agents that aims to enhance the experience of humans. Specifically, we model the experience of humans as the goals they expect to achieve during the task. We expect that agents should learn to enhance the extent to which humans achieve these goals while maintaining agents' original abilities (e.g., winning games). To achieve this, we propose the Reinforcement Learning from Human Gain (RLHG) approach. The RLHG approach introduces a "baseline", which corresponds to the extent to which humans primitively achieve their goals, and encourages agents to learn behaviors that can effectively enhance humans in achieving their goals better. We evaluate the RLHG agent in the popular Multi-player Online Battle Arena (MOBA) game, Honor of Kings, by conducting real-world human-agent tests. Both objective performance and subjective preference results show that the RLHG agent provides participants better gaming experience.

In human-robot cooperation, the robot cooperates with the human to accomplish the task together. Existing approaches assume the human has a specific goal during the cooperation, and the robot infers and acts toward it. However, in real-world environments, a human usually only has a general goal (e.g., general direction or area in motion planning) at the beginning of the cooperation which needs to be clarified to a specific goal (e.g., an exact position) during cooperation. The specification process is interactive and dynamic, which depends on the environment and the behavior of the partners. The robot that does not consider the goal specification process may cause frustration to the human partner, elongate the time to come to an agreement, and compromise or fail team performance. We present Evolutionary Value Learning (EVL) approach which uses a State-based Multivariate Bayesian Inference method to model the dynamics of goal specification process in HRC, and an Evolutionary Value Updating method to actively enhance the process of goal specification and cooperation formation. This enables the robot to simultaneously help the human to specify the goal and learn a cooperative policy in a Reinforcement Learning manner. In experiments with real human subjects, the robot equipped with EVL outperforms existing methods with faster goal specification processes and better team performance.

In a classic experiment on human social intelligence by psychologists Felix Warneken and Michael Tomasello, an 18-month old toddler watches a man carry a stack of books towards an unopened cabinet. When the man reaches the cabinet, he clumsily bangs the books against the door of the cabinet several times, then makes a puzzled noise. Something remarkable happens next: the toddler offers to help. Having inferred the man's goal, the toddler walks up to the cabinet and opens its doors, allowing the man to place his books inside. But how is the toddler, with such limited life experience, able to make this inference?