2025

1. [C94] Learn With Imagination: Safe Set Guided State-wise Constrained Policy Optimization
   Feihan Li, Yifan Sun, Weiye Zhao, Rui Chen, Tianhao Wei and Changliu Liu
   Learning for Dynamics and Control Conference, 2025
   

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   Deep reinforcement learning (RL) excels in various control tasks, yet the absence of safety guarantees hampers its real-world applicability. In particular, explorations during learning usually results in safety violations, while the RL agent learns from those mistakes. On the other hand, safe control techniques ensure persistent safety satisfaction but demand strong priors on system dynamics, which is usually hard to obtain in practice. To address these problems, we present Safe Set Guided State-wise Constrained Policy Optimization (S-3PO), a pioneering algorithm generating state-wise safe optimal policies with zero training violations, i.e., learning without mistakes. S-3PO first employs a safety-oriented monitor with black-box dynamics to ensure safe exploration. It then enforces an "imaginary" cost for the RL agent to converge to optimal behaviors within safety constraints. S-3PO outperforms existing methods in high-dimensional robotics tasks, managing state-wise constraints with zero training violation. This innovation marks a significant stride towards real-world safe RL deployment.

2. [C95] Continual Learning and Lifting of Koopman Dynamics for Linear Control of Legged Robots
   Feihan Li, Abulikemu Abuduweili, Yifan Sun, Rui Chen, Weiye Zhao and Changliu Liu
   Learning for Dynamics and Control Conference, 2025
   

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   The control of legged robots, particularly humanoid and quadruped robots, presents significant challenges due to their high-dimensional and nonlinear dynamics. While linear systems can be effectively controlled using methods like Model Predictive Control (MPC), the control of nonlinear systems remains complex. One promising solution is the Koopman Operator, which approximates nonlinear dynamics with a linear model, enabling the use of proven linear control techniques. However, achieving accurate linearization through data-driven methods is difficult due to issues like approximation error, domain shifts, and the limitations of fixed linear state-space representations. These challenges restrict the scalability of Koopman-based approaches. This paper addresses these challenges by proposing a continual learning algorithm designed to iteratively refine Koopman dynamics for high-dimensional legged robots. The key idea is to progressively expand the dataset and latent space dimension, enabling the learned Koopman dynamics to converge towards accurate approximations of the true system dynamics. Theoretical analysis shows that the linear approximation error of our method converges monotonically. Experimental results demonstrate that our method achieves high control performance on robots like Unitree G1/H1/A1/Go2 and ANYmal D, across various terrains using simple linear MPC controllers. This work is the first to successfully apply linearized Koopman dynamics for locomotion control of high-dimensional legged robots, enabling a scalable model-based control solution.

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1. [W] Calibrating AI Trust in Complementary Human-AI Collaboration
   Hanjiang Hu, Yifan Sun and Changliu Liu
   ICRA 2025 Workshop on Workshop on Public Trust in Autonomous Systems, 2025
   

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   Human-AI collaboration is a powerful paradigm in decision-making systems, where humans and AI contribute different strengths with clear complementarity. Yet, achieving optimal team performance depends critically on proper trust in AI, ensuring humans rely on AI appropriately. In real-world scenarios, humans often lack the expertise or performance transparency to judge AI accuracy directly, creating a gap in appropriate trust calibration. In this paper, we address this challenge through three key contributions: (1) we propose a theoretical framework modeling the evolution of human trust in AI over time under AI performance uncertainty, (2) we investigate two self-calibrating trust methods, an instance-based cognitive model and a reinforcement learning (RL) model that learns trust calibration policies from experience, and (3) we conduct simulations comparing both approaches against a rule-based baseline under dynamically varying AI performance. Results show that RL-based trust calibration outperforms others in cumulative performance, while instance-based calibration offers interpretability and sample efficiency. These findings offer pathways for safe and adaptive trust alignment in human-AI collaboration toward trustworthy autonomy.

1. [U] SPARK: A Modular Benchmark for Humanoid Robot Safety
   Yifan Sun, Rui Chen, Kai S Yun, Yikuan Fang, Sebin Jung, Feihan Li, Bowei Li, Weiye Zhao and Changliu Liu
   arXiv preprint arXiv:2502.03132, 2025
   

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   This paper introduces the Safe Protective and Assistive Robot Kit (SPARK), a comprehensive benchmark designed to ensure safety in humanoid autonomy and teleoperation. Humanoid robots pose significant safety risks due to their physical capabilities of interacting with complex environments. The physical structures of humanoid robots further add complexity to the design of general safety solutions. To facilitate the safe deployment of complex robot systems, SPARK can be used as a toolbox that comes with state-of-the-art safe control algorithms in a modular and composable robot control framework. Users can easily configure safety criteria and sensitivity levels to optimize the balance between safety and performance. To accelerate humanoid safety research and development, SPARK provides a simulation benchmark that compares safety approaches in a variety of environments, tasks, and robot models. Furthermore, SPARK allows quick deployment of synthesized safe controllers on real robots. For hardware deployment, SPARK supports Apple Vision Pro (AVP) or a Motion Capture System as external sensors, while also offering interfaces for seamless integration with alternative hardware setups. This paper demonstrates SPARK’s capability with both simulation experiments and case studies with a Unitree G1 humanoid robot. Leveraging these advantages of SPARK, users and researchers can significantly improve the safety of their humanoid systems as well as accelerate relevant research. The open-source code is available at (https://github.com/intelligent-control-lab/spark)

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2. [U] Dexterous Safe Control for Humanoids in Cluttered Environments via Projected Safe Set Algorithm
   Rui Chen, Yifan Sun and Changliu Liu
   arXiv preprint arXiv:2502.02858, 2025
   

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   It is critical to ensure safety for humanoid robots in real-world applications without compromising performance. In this paper, we consider the problem of dexterous safety, featuring limb-level geometry constraints for avoiding both external and self-collisions in cluttered environments. Compared to safety with simplified bounding geometries in sprase environments, dexterous safety produces numerous constraints which often lead to infeasible constraint sets when solving for safe robot control. To address this issue, we propose Projected Safe Set Algorithm (p-SSA), an extension of classical safe control algorithms to multi-constraint cases. p-SSA relaxes conflicting constraints in a principled manner, minimizing safety violations to guarantee feasible robot control. We verify our approach in simulation and on a real Unitree G1 humanoid robot performing complex collision avoidance tasks. Results show that p-SSA enables the humanoid to operate robustly in challenging situations with minimal safety violations and directly generalizes to various tasks with zero parameter tuning.