Projects

Publications

2026

[C118] λ-Reachability: Geometric-Horizon Safety Bellman Equations for Humanoid Safety
Rui Chen, Shangtao Li, Yifan Sun and Changliu Liu
Conference on Robot Learning, 2026
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We introduce λ-Reachability, a scalable approach to Hamilton–Jacobi safety analysis for high-dimensional robotic systems. Unlike prior discounted formulations that rely on fixed one-step Bellman updates, λ-Reachability employs a stochastic multi-step estimator of the safety value, using a geometrically distributed rollout horizon together with a randomly absorbed terminal. Conceptually analogous to TD(λ), λ-Reachability interpolates between local self-consistency updates and long-horizon max-over-trajectory safety targets via an interpretable horizon-control parameter. Unlike TD(λ), where the terminal value is always incorporated in learning targets, the terminal safety value in λ-Reachability is only used at a probability controlled by parameter δ. We formally show that for δ<1, the update induces a contraction mapping that allows temporal-difference learning; as λ\to 1, the estimator recovers the undiscounted reachability objective. We apply λ-Reachability to high-dimensional safety learning problems with both simulated and real humanoid robots under balance and collision avoidance constraints. Experimental results demonstrate that λ-Reachability significantly improves both safe-set boundary classification and safety margin estimation compared to single-step temporal-difference baselines.

2025

[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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[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.
[C90] Safe Control of Quadruped in Varying Dynamics via Safety Index Adaptation
Kai S. Yun, Rui Chen, Chase Dunaway, John M. Dolan and Changliu Liu
IEEE International Conference on Robotics and Automation, 2025
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Varying dynamics pose a fundamental difficulty when deploying safe control laws in the real world. Safety Index Synthesis (SIS) deeply relies on the system dynamics and once the dynamics change, the previously synthesized safety index becomes invalid. In this work, we show the real-time efficacy of Safety Index Adaptation (SIA) in varying dynamics. SIA enables real-time adaptation to the changing dynamics so that the adapted safe control law can still guarantee 1) forward invariance within a safe region and 2) finite time convergence to that safe region. This work employs SIA on a package-carrying quadruped robot, where the payload weight changes in real-time. SIA updates the safety index when the dynamics change, e.g., a change in payload weight, so that the quadruped can avoid obstacles while achieving its performance objectives. Numerical study provides theoretical guarantees for SIA and a series of hardware experiments demonstrate the effectiveness of SIA in real-world deployment in avoiding obstacles under varying dynamics.

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[C101] SPARK: Safe Protective and Assistive Robot Kit
Yifan Sun, Rui Chen, Kai S Yun, Yikuan Fang, Sebin Jung, Feihan Li, Bowei Li, Weiye Zhao and Changliu Liu
IFAC Symposium on Robotics, 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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[U] Dexterous Safe Control for Humanoids in Cluttered Environments via Projected Safe Set Algorithm
Rui Chen, Yifan Sun and Changliu Liu
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.

2024

[J24] State-wise Constrained Policy Optimization
Weiye Zhao, Rui Chen, Yifan Sun, Tianhao Wei and Changliu Liu
Transactions on Machine Learning Research, 2024
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[J23] Guard: A safe reinforcement learning benchmark
Weiye Zhao, Rui Chen, Yifan Sun, Ruixuan Liu, Tianhao Wei and Changliu Liu
Transactions on Machine Learning Research, 2024
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[C83] Meta-Control: Automatic Model-based Control Synthesis for Heterogeneous Robot Skills
Tianhao Wei, Liqian Ma, Rui Chen, Weiye Zhao and Changliu Liu
Conference on Robot Learning, 2024
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The requirements for real-world manipulation tasks are diverse and often conflicting; some tasks require precise motion while others require force compliance; some tasks require avoidance of certain regions while others require convergence to certain states. Satisfying these varied requirements with a fixed state-action representation and control strategy is challenging, impeding the development of a universal robotic foundation model. In this work, we propose Meta-Control, the first LLM-enabled automatic control synthesis approach that creates customized state representations and control strategies tailored to specific tasks. Our core insight is that a meta-control system can be built to automate the thought process that human experts use to design control systems. Specifically, human experts heavily use a model-based, hierarchical (from abstract to concrete) thought model, then compose various dynamic models and controllers together to form a control system. Meta-Control mimics the thought model and harnesses LLM’s extensive control knowledge with Socrates’ "art of midwifery" to automate the thought process. Meta-Control stands out for its fully model-based nature, allowing rigorous analysis, generalizability, robustness, efficient parameter tuning, and reliable real-time execution.

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[C77] Absolute Policy Optimization: Enhancing Lower Probability Bound of Performance with High Confidence
Weiye Zhao, Feihan Li, Yifan Sun, Rui Chen, Tianhao Wei and Changliu Liu
International Conference on Machine Learning, 2024
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[C70] Real-time Safety Index Adaptation for Parameter-varying Systems via Determinant Gradient Ascend
Rui Chen, Weiye Zhao, Ruixuan Liu, Weiyang Zhang and Changliu Liu
American control Conference, 2024
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[C68] An Optimal Control Framework for Influencing Human Driving Behavior in Mixed-Autonomy Traffic
Anirudh Chari, Rui Chen, Jaskaran Grover and Changliu Liu
American Control Conference, 2024
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[C67] Safety Index Synthesis with State-dependent Control Space
Rui Chen, Weiye Zhao and Changliu Liu
American Control Conference, 2024
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[U] Absolute State-wise Constrained Policy Optimization: High-Probability State-wise Constraints Satisfaction
Weiye Zhao, Feihan Li, Yifan Sun, Yujie Wang, Rui Chen, Tianhao Wei and Changliu Liu
arXiv:2410.01212, 2024
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Enforcing state-wise safety constraints is critical for the application of reinforcement learning (RL) in real-world problems, such as autonomous driving and robot manipulation. However, existing safe RL methods only enforce state-wise constraints in expectation or enforce hard state-wise constraints with strong assumptions. The former does not exclude the probability of safety violations, while the latter is impractical. Our insight is that although it is intractable to guarantee hard state-wise constraints in a model-free setting, we can enforce state-wise safety with high probability while excluding strong assumptions. To accomplish the goal, we propose Absolute State-wise Constrained Policy Optimization (ASCPO), a novel general-purpose policy search algorithm that guarantees high-probability state-wise constraint satisfaction for stochastic systems. We demonstrate the effectiveness of our approach by training neural network policies for extensive robot locomotion tasks, where the agent must adhere to various state-wise safety constraints. Our results show that ASCPO significantly outperforms existing methods in handling state-wise constraints across challenging continuous control tasks, highlighting its potential for real-world applications.

2023

[J17] Robust and context-aware real-time collaborative robot handling via dynamic gesture commands
Rui Chen, Alvin Shek and Changliu Liu
IEEE Robotics and Automation Letters, 2023
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[C64] Interactive Car-Following: Matters but NOT Always
Chengyuan Zhang, Rui Chen, Jiacheng Zhu, Wenshuo Wang, Changliu Liu and Lijun Sun
IEEE International Conference on Intelligent Transportation Systems, 2023
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[C62] Proactive human-robot co-assembly: Leveraging human intention prediction and robust safe control
Ruixuan Liu, Rui Chen, Abulikemu Abuduweili and Changliu Liu
IEEE Conference on Control Technology and Applications, 2023
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Human-robot collaboration (HRC) is one key component to achieving flexible manufacturing to meet the different needs of customers. However, it is difficult to build intelligent robots that can proactively assist humans in a safe and efficient way due to several challenges. First, it is challenging to achieve efficient collaboration due to diverse human behaviors and data scarcity. Second, it is difficult to ensure interactive safety due to uncertainty in human behaviors. This paper presents an integrated framework for proactive HRC. A robust intention prediction module, which leverages prior task information and human-in-the-loop training, is learned to guide the robot for efficient collaboration. The proposed framework also uses robust safe control to ensure interactive safety under uncertainty. The developed framework is applied to a co-assembly task using a Kinova Gen3 robot. The experiment demonstrates that our solution is robust to environmental changes as well as different human preferences and behaviors. In addition, it improves task efficiency by approximately 15-20%. Moreover, the experiment demonstrates that our solution can guarantee interactive safety during proactive collaboration.
[C60] State-wise safe reinforcement learning: A survey
Weiye Zhao, Tairan He, Rui Chen, Tianhao Wei and Changliu Liu
International Joint Conferences on Artificial Intelligence, 2023
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[C58] Space-Time Conflict Spheres for Constrained Multi-Agent Motion Planning
Anirudh Chari, Rui Chen and Changliu Liu
IEEE Intelligent Vehicles Symposium, 2023
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[C55] Learning from physical human feedback: An object-centric one-shot adaptation method
Alvin Shek, Bo Ying Su, Rui Chen and Changliu Liu
IEEE International Conference on Robotics and Automation, 2023
Outstanding Interaction Paper
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2022

[C50] Task-agnostic Adaptation for Safe Human-robot Handover
Ruixuan Liu, Rui Chen and Changliu Liu
IFAC Workshop on Cyber-Physical Human Systems, 2022
Best Student Paper Award
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[C45] A Composable Framework for Policy Design, Learning, and Transfer Toward Safe and Efficient Industrial Insertion
Rui Chen, Chenxi Wang, Tianhao Wei and Changliu Liu
IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022
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Delicate industrial insertion tasks (e.g., PC board assembly) remain challenging for industrial robots. The challenges include low error tolerance, delicacy of the components, and large task variations with respect to the components to be inserted. To deliver a feasible robotic solution for these insertion tasks, we also need to account for hardware limits of existing robotic systems and minimize the integration effort. This paper proposes a composable framework for efficient integration of a safe insertion policy on existing robotic platforms to accomplish these insertion tasks. The policy has an interpretable modularized design and can be learned efficiently on hardware and transferred to new tasks easily. In particular, the policy includes a safe insertion agent as a baseline policy for insertion, an optimal configurable Cartesian tracker as an interface to robot hardware, a probabilistic inference module to handle component variety and insertion errors, and a safe learning module to optimize the parameters in the aforementioned modules to achieve the best performance on designated hardware. The experiment results on a UR10 robot show that the proposed framework achieves safety (for the delicacy of components), accuracy (for low tolerance), robustness (against perception error and component defection), adaptability and transferability (for task variations), as well as task efficiency during execution plus data and time efficiency during learning.

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[C44] Jerk-bounded Position Controller with Real-Time Task Modification for Interactive Industrial Robots
Ruixuan Liu, Rui Chen, Yifan Sun, Yu Zhao and Changliu Liu
IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2022
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Industrial robots are widely used in many applications with structured and deterministic environments. However, the contemporary need requires industrial robots to intelligently operate in dynamic environments. It is challenging to design a safe and efficient robotic system with industrial robots in a dynamic environment for several reasons. First, most industrial robots require the input to have specific formats, which takes additional efforts to convert from task-level user commands. Second, existing robot drivers do not support overwriting ongoing tasks in real-time, which hinders the robot from responding to the dynamic environment. Third, most industrial robots only expose motion-level control, making it challenging to enforce dynamic constraints during trajectory tracking. To resolve the above challenges, this paper presents a jerk-bounded position control driver (JPC) for industrial robots. JPC provides a unified interface for tracking complex trajectories and is able to enforce dynamic constraints using motion-level control, without accessing servo-level control. Most importantly, JPC enables real-time trajectory modification. Users can overwrite the ongoing task with a new one without violating dynamic constraints. The proposed JPC is implemented and tested on the FANUC LR Mate 200id/7L robot with both artificially generated data and an interactive robot handover task. Experiments show that the proposed JPC can track complex trajectories accurately within dynamic limits and seamlessly switch to new trajectory references before the ongoing task ends.

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[C42] Safe Interactive Industrial Robots using Jerk-based Safe Set Algorithm
Ruixuan Liu, Rui Chen and Changliu Liu
International Symposium on Flexible Automation, 2022
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The need to increase the flexibility of production lines is calling for robots to collaborate with human workers. However, existing interactive industrial robots only guarantee intrinsic safety (reduce collision impact), but not interactive safety (collision avoidance), which greatly limited their flexibility. The issue arises from two limitations in existing control software for industrial robots: 1) lack of support for real-time trajectory modification; 2) lack of intelligent safe control algorithms with guaranteed collision avoidance under robot dynamics constraints. To address the first issue, a jerk-bounded position controller (JPC) was developed previously. This paper addresses the second limitation, on top of the JPC. Specifically, we introduce a jerk-based safe set algorithm (JSSA) to ensure collision avoidance while considering the robot dynamics constraints. The JSSA greatly extends the scope of the original safe set algorithm, which has only been applied for second-order systems with unbounded accelerations. The JSSA is implemented on the FANUC LR Mate 200id/7L robot and validated with HRI tasks. Experiments show that the JSSA can consistently keep the robot at a safe distance from the human while executing the designated task.

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