Shielded Planning Guided Data-Efficient and Safe Reinforcement Learning.

Safe reinforcement learning (RL) has shown great potential for building safe general-purpose robotic systems. While many existing works have focused on post-training policy safety, it remains an open problem to ensure safety during training as well as to improve exploration efficiency. Motivated to address these challenges, this work develops shielded planning guided policy optimization (SPPO), a new model-based safe RL method that augments policy optimization algorithms with path planning and shielding mechanism.

Task-Driven Reinforcement Learning With Action Primitives for Long-Horizon Manipulation Skills.

It is an interesting open problem to enable robots to efficiently and effectively learn long-horizon manipulation skills. Motivated to augment robot learning via more effective exploration, this work develops task-driven reinforcement learning with action primitives (TRAPs), a new manipulation skill learning framework that augments standard reinforcement learning algorithms with formal methods and parameterized action space (PAS). In particular, TRAPs uses linear temporal logic (LTL) to specify complex manipulation skills.

Safe reinforcement learning under temporal logic with reward design and quantum action selection.

This paper proposes an advanced Reinforcement Learning (RL) method, incorporating reward-shaping, safety value functions, and a quantum action selection algorithm. The method is model-free and can synthesize a finite policy that maximizes the probability of satisfying a complex task. Although RL is a promising approach, it suffers from unsafe traps and sparse rewards and becomes impractical when applied to real-world problems.

Divergent Component of Motion Planning and Adaptive Repetitive Control for Wearable Walking Exoskeletons.

Wearable walking exoskeletons show great potentials in helping patients with neuro musculoskeletal stroke. Key to the successful applications is the design of effective walking trajectories that enable smooth walking for exoskeletons. This work proposes a walking planning method based on the divergent component of motion to obtain a stable joint angle trajectory.

Asymmetric Cooperation Control of Dual-Arm Exoskeletons Using Human Collaborative Manipulation Models.

The exoskeleton is mainly used by subjects who suffer muscle injury to enhance motor ability in the daily life environment. Previous research seldom considers extending human collaboration skills to human-robot collaborations. In this article, two models, that is: 1) the following the better model and 2) the interpersonal goal integration model, are designed to facilitate the human-human collaborative manipulation in tracking a moving target.

Reference Trajectory Reshaping Optimization and Control of Robotic Exoskeletons for Human-Robot Co-Manipulation.

For human-robot co-manipulation by robotic exoskeletons, the interaction forces provide a communication channel through which the human and the robot can coordinate their actions. In this article, an optimization approach for reshaping the physical interactive trajectory is presented in the co-manipulation tasks, which combines impedance control to enable the human to adjust both the desired and the actual trajectories of the robot. Different from previous studies, the proposed method significantly reshapes the desired trajectory during physical human-robot interaction (pHRI) based on force feedback, without requiring constant human guidance.

Controllability Ensured Leader Group Selection on Signed Multiagent Networks.

Leader-follower controllability on signed multiagent networks is investigated in this paper. Specifically, we consider a dynamic signed multiagent network, where the agents interact via neighbor-based Laplacian feedback and the network allows positive and negative edges to capture cooperative and competitive interactions among agents. The agents are classified as either leaders or followers, thus forming a leader-follower signed network.

A foodborne acute gastroenteritis outbreak caused by GII.P16-GII.2 norovirus in a boarding high school, Beijing, China: a case-control study.

Objective: In December 2017, an acute gastroenteritis outbreak involving 61 students occurred in a boarding high school in Beijing, China. We conducted an outbreak investigation immediately in order to determine the cause of this outbreak and provide effective control measures.

Results: The laboratory inspection showed that this outbreak was caused by GII.

Synchronization of Uncertain Euler-Lagrange Systems With Uncertain Time-Varying Communication Delays.

A decentralized controller is designed for leader-based synchronization of communication-delayed networked agents. The agents have heterogeneous dynamics modeled by uncertain, nonlinear Euler-Lagrange equations of motion affected by heterogeneous, unknown, exogenous disturbances. The developed controller requires only one-hop (delayed) communication from network neighbors and the communication delays are assumed to be heterogeneous, uncertain, and time-varying.