Projects

Proactive Safe Human-Robot Interaction

Publications

2026

[C108] From Refusal to Recovery: A Control-Theoretic Approach to Generative AI Guardrails
Ravi Pandya, Madison Bland, Duy P. Nguyen, Changliu Liu, Jaime F. Fisac and Andrea Bajcsy
Second Conference of the International Association for Safe and Ethical AI (IASEAI), 2026
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2025

[C89] Robots that Learn to Safely Influence via Prediction-Informed Reach-Avoid Dynamic Games
Ravi Pandya, Changliu Liu and Andrea Bajcsy
IEEE International Conference on Robotics and Automation, 2025
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Abstract:

Robots can influence people to accomplish their tasks more efficiently: autonomous cars can inch forward at an intersection to pass through, and tabletop manipulators can go for an object on the table first. However, a robot’s ability to influence can also compromise the safety of nearby people if naively executed. In this work, we pose and solve a novel robust reach-avoid dynamic game which enables robots to be maximally influential, but only when a safety backup control exists. On the human side, we model the human’s behavior as goal-driven but conditioned on the robot’s plan, enabling us to capture influence. On the robot side, we solve the dynamic game in the joint physical and belief space, enabling the robot to reason about how its uncertainty in human behavior will evolve over time. We instantiate our method, called SLIDE (Safely Leveraging Influence in Dynamic Environments), in a high-dimensional (39-D) simulated human-robot collaborative manipulation task solved via offline game-theoretic reinforcement learning. We compare our approach to a robust baseline that treats the human as a worst-case adversary, a safety controller that does not explicitly reason about influence, and an energy-function-based safety shield. We find that SLIDE consistently enables the robot to leverage the influence it has on the human when it is safe to do so, ultimately allowing the robot to be less conservative while still ensuring a high safety rate during task execution.

[T6] Influence-Aware Safety for Human-Robot Interaction
Ravi Pandya
PhD Thesis, 2025
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Abstract:

In recent years, we have seen through recommender systems on social media how influential (and potentially harmful) algorithms can be in our lives, sometimes creating polarization and conspiracies that lead to unsafe behavior. Now that robots are also growing more common in the real world, we must be very careful to ensure that they are aware of the influence they will have on people, especially when it comes to safety—we do not want robots to cause any physical harm. In this thesis, we focus on the problem of influence-aware safe control for human-robot interaction in hopes of enabling robots to intentionally and positively influence people to make their interactions with robots safer and more efficient. We first study this problem from the safe control perspective by introducing a novel method for dealing with the multimodality of the robot’s uncertainty over a human’s intention inside a robust safe controller. Next, we explore different methods for generating influence-aware robot behavior from different levels of abstraction: action-directed, goal-directed, and strategy-directed. We ultimately find useful tools for designing robot behaviors that can proactively influence human collaborators towards positive outcomes. Finally, we join these thrusts by posing and solving the full influence-aware safety problem for human-robot interactions. We first show that explicitly enabling a robot to reason about the influence it has on a human collaborator can lead to more efficient interactions without sacrificing safety. Finally, we extend this paradigm to more general human-AI interactions, particularly people interacting with large language models (LLMs). We show that treating safety as failures in the physical environment in this domain allows us to automatically learn safety-preserving language actions and influential behavior that can steer interactions towards safe long-term outcomes from budget constraints in e-commerce to collision avoidance in driving. The work done in this thesis establishes a basis for several key research directions in the future. Specifically, researchers in AI safety can build on the influence-aware safety problem as a basis for generating safe AI systems that are explicitly aware of how their actions may change the behavior of human users in the real world. This work also enables future work to focus on safe long-term interactions between humans and robots. While this thesis focuses on influence during single interactions, the influence-aware safety problem can easily be applied to repeated interactions with human users, which is an important directions as AI chatbots, recommendation systems and physical robots continue becoming larger parts of our daily lives. Ultimately, this thesis will help researchers understand the importance of explicitly modeling the influence that autonomous agents have on people and how to use this influence to keep people safe.

2024

[C73] Multi-Agent Strategy Explanations for Human-Robot Collaboration
Ravi Pandya, Michelle Zhao, Changliu Liu, Reid Simmons and Henny Admoni
IEEE International Conference on Robotics and Automation, 2024
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[C72] Towards Proactive Safe Human-Robot Collaborations via Data-Efficient Conditional Behavior Prediction
Ravi Pandya, Zhuoyuan Wang, Yorie Nakahira and Changliu Liu
IEEE International Conference on Robotics and Automation, 2024
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[C66] Multimodal Safe Control for Human-Robot Interaction
Ravi Pandya, Tianhao Wei and Changliu Liu
American Control Conference, 2024
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2023

[U] Robust Safe Control with Multi-Modal Uncertainty
Tianhao Wei, Liqian Ma, Ravi Pandya and Changliu Liu
arXiv:2309.16830, 2023
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2022

[C46] Safe and Efficient Exploration of Human Models During Human-Robot Interaction
Ravi Pandya and Changliu Liu
IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022
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Abstract:

Many collaborative human-robot tasks require the robot to stay safe and work efficiently around humans. Since the robot can only stay safe with respect to its own model of the human, we want the robot to learn a good model of the human in order to act both safely and efficiently. This paper studies methods that enable a robot to safely explore the space of a human-robot system to improve the robot’s model of the human, which will consequently allow the robot to access a larger state space and better work with the human. In particular, we introduce active exploration under the framework of energy-function based safe control, investigate the effect of different active exploration strategies, and finally analyze the effect of safe active exploration on both analytical and neural network human models.

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