This study presents an innovative reinforcement learning (RL) control approach to facilitate soft exosuit-assisted human walking. Our goal is to address the ongoing challenges in developing reliable RL-based methods for controlling physical devices. To overcome key obstacles—such as limited data, the absence of a simulator for human-robot interaction during walking, the need for low computational overhead in real-time deployment, and the demand for rapid adaptation to achieve personalized control while ensuring human safety—we propose an online Adaptation from an offline Imitating Expert Policy (AIP) approach. Our offline learning mimics human expert actions through real human walking demonstrations without robot assistance. The resulted policy is then used to initialize online actor-critic learning, the goal of which is to optimally personalize robot assistance. In addition to being fast and robust, our online RL method also posses important properties such as learning convergence, dynamic stability, and solution optimality. We have successfully demonstrated our simple and robust framework for safe robot control on all five tested human participants, without selectively presenting results. The qualitative performance guarantees provided by our online RL, along with the consistent experimental validation of AIP control, represent the first demonstration of online adaptation for softsuit control personalization and serve as important evidence for the use of online RL in controlling a physical device to solve a real-life problem.

Wearable lower limb robots are promising technologies to assist human locomotion. Soft robotic exosuits introduce a promising solution for reducing muscle effort and metabolic cost as they are lightweight, transparent and inherently safe. However, it is challenging to effectively control such soft robots and personalize the assistance for individual users. With the difficulty in developing robust dynamic model of the human-soft robot system, especially the interacting dynamics between the human and the robot, traditional control methods have seen limited success in addressing these challenges. Reinforcement learning (RL), a data-driven optimal control method, provides a naturally promising alternative. In this study, we propose an innovative control design approach to enable human normative walking with reduced physical effort. To achieve this goal, we propose to first offline learn an exosuit controller for typical human normative walking which is then used in the online phase of control tuning for individual users. Four participants are recruited to test the exosuit controller in treadmill walking. Our results show that online tuning for individual users reaches convergence quickly, typically in one experimental trial due to using an efficient offline pre-trained policy. Furthermore, the RL control of the exosuit results in an average muscle effort reduction of 8.8% and 2.8% for the vastus lateralis and biceps femoris as measured by electromyography (EMG) sensors. These results provide the first evidence of customizing the soft exosuit assistance for individual users.

We propose a method for designing global nonlinear controllers based on the application of memory-based learning schemes for the purpose of aggregating multiple solutions produced by optimal control algorithms based on differential dynamic programming. The method leverages the fact that these optimal control algorithms produce not only nominal state and control trajectories, but entire full-state feedback (FSF) controllers, and the combined controller effectively switches between these multiple FSF controllers. Empirical verification demonstrates that it can be very effective in solving difficult benchmark control problems at high control rates.

The paper presents an experimental study on the application of deep reinforcement learning (DRL) methods to the problem of optimally transporting cargo loads by an overhead gantry crane in minimal time. Experiments in simulation using a physics engine on two versions of the problem, with two and four degrees of freedom and employing reward functions that reflect the objective of load stabilization in minimal time, demonstrate that policies trained with the Stochastic Actor Critic (SAC) DRL method achieve up to 20% shorter transport time in comparison with controllers designed by means of more traditional methods from the field of control engineering.

We introduce a new stage reward estimator named the long $N$-step surrogate stage (LNSS) reward for deep reinforcement learning (RL). It aims at mitigating the high variance problem, which has shown impeding successful convergence of learning, hurting task performance, and hindering applications of deep RL in continuous control problems. In this paper we show that LNSS, which utilizes a long reward trajectory of rewards of future steps, provides consistent performance improvement measured by average reward, convergence speed, learning success rate,and variance reduction in $Q$ values and rewards. Our evaluations are based on a variety of environments in DeepMind Control Suite and OpenAI Gym by using LNSS in baseline deep RL algorithms such as DDPG, D4PG, and TD3. We show that LNSS reward has enabled good results that have been challenging to obtain by deep RL previously. Our analysis also shows that LNSS exponentially reduces the upper bound on the variances of $Q$ values from respective single-step methods.

This is the first attempt at considering human influence in the reinforcement learning control of a robotic lower limb prosthesis toward symmetrical walking in real world situations. We propose a collaborative multi-agent reinforcement learning (cMARL) solution framework for this highly complex and challenging human-prosthesis collaboration (HPC) problem. The design of an automatic controller of the robot within the HPC context is based on accessible physical features or measurements that are known to affect walking performance. Comparisons are made with the current state-of-the-art robot control designs, which are single-agent based, as well as existing MARL solution approaches tailored to the problem, including multi-agent deep deterministic policy gradient (MADDPG) and counterfactual multi-agent policy gradient (COMA). Results show that, when compared to these approaches, treating the human and robot as coupled agents and using estimated human adaption in robot control design can achieve lower stage cost, peak error, and symmetry value to ensure better human walking performance. Additionally, our approach accelerates learning of walking tasks and increases learning success rate. The proposed framework can potentially be further developed to examine how human and robotic lower limb prosthesis interact, an area that little is known about. Advancing cMARL toward real world applications such as HPC for normative walking sets a good example of how AI can positively impact on people’s lives.