4 citations · 21 across the 25 of their papers we have counts for
25 papers
SPARK: Safe Protective and Assistive Robot Kit
Yifan Sun, Rui Chen, Kai S. Yun +6
This paper introduces the Safe Protective and Assistive Robot Kit (SPARK), a comprehensive benchmark designed to ensure safety in humanoid autonomy and teleoperation. Humanoid robo…
Continual Learning and Lifting of Koopman Dynamics for Linear Control of Legged Robots
Feihan Li, Abulikemu Abuduweili, Yifan Sun +3
The control of legged robots, particularly humanoid and quadruped robots, presents significant challenges due to their high-dimensional and nonlinear dynamics. While linear systems…
Absolute State-wise Constrained Policy Optimization: High-Probability State-wise Constraints Satisfaction
Weiye Zhao, Feihan Li, Yifan Sun +4
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. Ho…
WeHelp: A Shared Autonomy System for Wheelchair Users
Abulikemu Abuduweili, Alice Wu, Tianhao Wei +1
There is a large population of wheelchair users. Most of the wheelchair users need help with daily tasks. However, according to recent reports, their needs are not properly satisfi…
Physics-Aware Combinatorial Assembly Sequence Planning using Data-free Action Masking
Ruixuan Liu, Alan Chen, Weiye Zhao +1
Combinatorial assembly uses standardized unit primitives to build objects that satisfy user specifications. This paper studies assembly sequence planning (ASP) for physical combina…
Meta-Control: Automatic Model-based Control Synthesis for Heterogeneous Robot Skills
Tianhao Wei, Liqian Ma, Rui Chen +2
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 av…
Implicit Safe Set Algorithm for Provably Safe Reinforcement Learning
Weiye Zhao, Feihan Li, Changliu Liu
Deep reinforcement learning (DRL) has demonstrated remarkable performance in many continuous control tasks. However, a significant obstacle to the real-world application of DRL is…
Real-time Safety Index Adaptation for Parameter-varying Systems via Determinant Gradient Ascend
Rui Chen, Weiye Zhao, Ruixuan Liu +2
Safety Index Synthesis (SIS) is critical for deriving safe control laws. Recent works propose to synthesize a safety index (SI) via nonlinear programming and derive a safe control…
Learning Predictive Safety Filter via Decomposition of Robust Invariant Set
Zeyang Li, Chuxiong Hu, Weiye Zhao +1
Ensuring safety of nonlinear systems under model uncertainty and external disturbances is crucial, especially for real-world control tasks. Predictive methods such as robust model…
Absolute Policy Optimization
Weiye Zhao, Feihan Li, Yifan Sun +3
In recent years, trust region on-policy reinforcement learning has achieved impressive results in addressing complex control tasks and gaming scenarios. However, contemporary state…
Safety Index Synthesis with State-dependent Control Space
Rui Chen, Weiye Zhao, Changliu Liu
This paper introduces an approach for synthesizing feasible safety indices to derive safe control laws under state-dependent control spaces. The problem, referred to as Safety Inde…
Learn With Imagination: Safe Set Guided State-wise Constrained Policy Optimization
Yifan Sun, Feihan Li, Weiye Zhao +3
Deep reinforcement learning (RL) excels in various control tasks, yet the absence of safety guarantees hampers its real-world applicability. In particular, explorations during lear…
State-wise Constrained Policy Optimization
Weiye Zhao, Rui Chen, Yifan Sun +2
Reinforcement Learning (RL) algorithms have shown tremendous success in simulation environments, but their application to real-world problems faces significant challenges, with saf…
GUARD: A Safe Reinforcement Learning Benchmark
Weiye Zhao, Yifan Sun, Feihan Li +4
Due to the trial-and-error nature, it is typically challenging to apply RL algorithms to safety-critical real-world applications, such as autonomous driving, human-robot interactio…
Hybrid Task Constrained Planner for Robot Manipulator in Confined Environment
Yifan Sun, Weiye Zhao, Changliu Liu
Trajectory generation in confined environment is crucial for wide adoption of intelligent robot manipulators. In this paper, we propose a novel motion planning approach for redunda…
State-wise Safe Reinforcement Learning: A Survey
Weiye Zhao, Tairan He, Rui Chen +2
Despite the tremendous success of Reinforcement Learning (RL) algorithms in simulation environments, applying RL to real-world applications still faces many challenges. A major con…
AutoCost: Evolving Intrinsic Cost for Zero-violation Reinforcement Learning
Tairan He, Weiye Zhao, Changliu Liu
Safety is a critical hurdle that limits the application of deep reinforcement learning (RL) to real-world control tasks. To this end, constrained reinforcement learning leverages c…
Probabilistic Safeguard for Reinforcement Learning Using Safety Index Guided Gaussian Process Models
Weiye Zhao, Tairan He, Changliu Liu
Safety is one of the biggest concerns to applying reinforcement learning (RL) to the physical world. In its core part, it is challenging to ensure RL agents persistently satisfy a…
Safety Index Synthesis via Sum-of-Squares Programming
Weiye Zhao, Tairan He, Tianhao Wei +2
Control systems often need to satisfy strict safety requirements. Safety index provides a handy way to evaluate the safety level of the system and derive the resulting safe control…
Persistently Feasible Robust Safe Control by Safety Index Synthesis and Convex Semi-Infinite Programming
Tianhao Wei, Shucheng Kang, Weiye Zhao +1
Model mismatches prevail in real-world applications. Ensuring safety for systems with uncertain dynamic models is critical. However, existing robust safe controllers may not be rea…
Provably Safe Tolerance Estimation for Robot Arms via Sum-of-Squares Programming
Weiye Zhao, Suqin He, Changliu Liu
Tolerance estimation problems are prevailing in engineering applications. For example, in modern robotics, it remains challenging to efficiently estimate joint tolerance, \ie the m…
Contact-Rich Trajectory Generation in Confined Environments Using Iterative Convex Optimization
Weiye Zhao, Suqin He, Chengtao Wen +1
Applying intelligent robot arms in dynamic uncertain environments (i.e., flexible production lines) remains challenging, which requires efficient algorithms for real time trajector…
Experimental Evaluation of Human Motion Prediction: Toward Safe and Efficient Human Robot Collaboration
Weiye Zhao, Liting Sun, Changliu Liu +1
Human motion prediction is non-trivial in modern industrial settings. Accurate prediction of human motion can not only improve efficiency in human robot collaboration, but also enh…
Safe Adaptation with Multiplicative Uncertainties Using Robust Safe Set Algorithm
Charles Noren, Weiye Zhao, Changliu Liu
Maintaining safety under adaptation has long been considered to be an important capability for autonomous systems. As these systems estimate and change the ego-model of the system…