publications

2026

1. IJCAI

   Empirical Evidence and Analysis of a Critical Pitfall in Reward Learning from Human Feedback

   Taha Shaheen ,  Stephen G. West ,  and  Yu Zhang

   In Proceedings of the 35th International Joint Conference on Artificial Intelligence (IJCAI-ECAI 2026), Aug 2026

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   Reward learning via human feedback is a crucial capability for beneficial AI. Current methods are built on decision-making theories that assume a matched dynamics model between the learning agent and the feedback provider. However, humans often form imperfect internal dynamics models, and their feedback reflects these misconceptions. While this relationship has long been hypothesised, its manifestation in sequential decision-making remains largely an assumption. Our work provides the first comprehensive empirical investigation of this relationship through a randomized controlled trial (N=211). We followed a two-stage design where we first initialized the participants’ understanding of the dynamics in a grid-world navigation domain and then manipulated it using text-based instructions. Causal mediation analysis revealed that humans’ internal models play a mediating role in feedback behaviour. We show that this relationship is invariant across visual contexts and is robust to three common feedback types: pairwise preferences, trajectory corrections, and off-switch interventions. These findings confirm a critical limitation of current reward learning methods and establish the missing psychological foundation for approaches that incorporate dynamics understanding.

   @inproceedings{shaheen2026criticalpitfall,
     title = {Empirical Evidence and Analysis of a Critical Pitfall in Reward Learning from Human Feedback},
     author = {Shaheen, Taha and West, Stephen G. and Zhang, Yu},
     booktitle = {Proceedings of the 35th International Joint Conference on Artificial Intelligence (IJCAI-ECAI 2026)},
     year = {2026},
     month = aug,
     url = {https://par.nsf.gov/servlets/purl/10683099},
   }

2025

1. arXiv

   Active Shadowing (ASD): Manipulating Visual Perception of Robotics Behaviors via Implicit Communication

   Andrew Boateng ,  Prakhar Bhartiya ,  Taha Shaheen , and 1 more author

   arXiv preprint arXiv:2407.01468, Aug 2025

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   Explicit communication is often valued for its directness in presenting information but requires attention during exchange, resulting in cognitive interruptions. On the other hand, implicit communication contributes to tacit and smooth interaction, making it more suitable for teaming, but requires inference for interpretation. This paper studies a novel type of implicit visual communication (IVC) using shadows via visual projection with augmented reality, referred to as active shadowing (ASD). Prior IVC methods, such as legible motion, are often used to influence the perception of robot behavior to make it more understandable. They often require changing the physical robot behavior, resulting in suboptimality. In our work, we investigate how ASD can be used to achieve similar effects without losing optimality. Our evaluations with user studies demonstrates that ASD can effectively creates ”illusions” that maintain optimal physical behavior without compromising its understandability. We also show that ASD can be more informative than other explicit communication methods, and examine the conditions under which ASD becomes less effective.

   @article{boateng2024active,
     title = {Active Shadowing (ASD): Manipulating Visual Perception of Robotics Behaviors via Implicit Communication},
     author = {Boateng, Andrew and Bhartiya, Prakhar and Shaheen, Taha and Zhang, Yu},
     journal = {arXiv preprint arXiv:2407.01468},
     year = {2025},
     url = {https://arxiv.org/abs/2407.01468},
   }

2024

1. ACM THRI

   Investigation of Low-Moral Actions by Malicious Anonymous Operators of Avatar Robots

   Taha Shaheen ,  Dražen Brščić ,  and  Takayuki Kanda

   ACM Transactions on Human-Robot Interaction, Sep 2024

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   Avatar robots allow a teleoperator to interact with the people and environment of a remote place. Malicious operators can use this technology to perpetrate malicious or low-moral actions. In this study, we used hazard identification workshops to identify low-moral actions that are possible through the locomotor movement, cameras, and microphones of an avatar robot. We conducted three workshops, each with four potential future users of avatars, to brainstorm possible low-moral actions. As avatars are not yet widespread, we gave participants experience with this technology by having them control both a simulated avatar and a real avatar as a malicious anonymous operator in a variety of situations. They also experienced sharing space with an avatar controlled by a malicious anonymous operator. We categorized the ideas generated from the workshops using affinity diagram analysis and identified four major categories: violate privacy and security, inhibit, annoy, and destroy or hurt. We also identified subcategories for each. In the second half of this study, we discuss all low-moral action subcategories in terms of their detection, mitigation, and prevention by studying literature from autonomous, social, teleoperated, and telepresence robots as well as other fields where relevant.

   @article{shaheen2024lowmoralactions,
     author = {Shaheen, Taha and Br\v{s}\v{c}i\'{c}, Dra\v{z}en and Kanda, Takayuki},
     title = {Investigation of Low-Moral Actions by Malicious Anonymous Operators of Avatar Robots},
     year = {2024},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3696466},
     doi = {10.1145/3696466},
     journal = {ACM Transactions on Human-Robot Interaction},
     month = sep,
     keywords = {avatar robots, low-moral actions, hazard identification, malicious users, ethics},
   }