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Abstract Background Few studies have systematically investigated robust controllers for lower limb rehabilitation exoskeletons (LLREs) that can safely and effectively assist users with a variety of neuromuscular disorders to walk with full autonomy. One of the key challenges for developing such a robust controller is to handle different degrees of uncertain human-exoskeleton interaction forces from the patients. Consequently, conventional walking controllers either are patient-condition specific or involve tuning of many control parameters, which could behave unreliably and even fail to maintain balance. Methods We present a novel, deep neural network, reinforcement learning-based robust controller for a LLRE based on a decoupled offline human-exoskeleton simulation training with three independent networks, which aims to provide reliable walking assistance against various and uncertain human-exoskeleton interaction forces. The exoskeleton controller is driven by a neural network control policy that acts on a stream of the LLRE’s proprioceptive signals, including joint kinematic states, and subsequently predicts real-time position control targets for the actuated joints. To handle uncertain human interaction forces, the control policy is trained intentionally with an integrated human musculoskeletal model and realistic human-exoskeleton interaction forces. Two other neural networks are connected with the control policy network to predict the interaction forces and muscle coordination. To further increase the robustness of the control policy to different human conditions, we employ domain randomization during training that includes not only randomization of exoskeleton dynamics properties but, more importantly, randomization of human muscle strength to simulate the variability of the patient’s disability. Through this decoupled deep reinforcement learning framework, the trained controller of LLREs is able to provide reliable walking assistance to patients with different degrees of neuromuscular disorders without any control parameter tuning. Results and conclusion A universal, RL-based walking controller is trained and virtually tested on a LLRE system to verify its effectiveness and robustness in assisting users with different disabilities such as passive muscles (quadriplegic), muscle weakness, or hemiplegic conditions without any control parameter tuning. Analysis of the RMSE for joint tracking, CoP-based stability, and gait symmetry shows the effectiveness of the controller. An ablation study also demonstrates the strong robustness of the control policy under large exoskeleton dynamic property ranges and various human-exoskeleton interaction forces. The decoupled network structure allows us to isolate the LLRE control policy network for testing and sim-to-real transfer since it uses only proprioception information of the LLRE (joint sensory state) as the input. Furthermore, the controller is shown to be able to handle different patient conditions without the need for patient-specific control parameter tuning. 

Abstract Soft robots can undergo large elastic deformations and adapt to complex shapes. However, they lack the structural strength to withstand external loads due to the intrinsic compliance of fabrication materials (silicone or rubber). In this paper, we present a novel stiffness modulation approach that controls the robot’s stiffness on-demand without permanently affecting the intrinsic compliance of the elastomeric body. Inspired by concentric tube robots, this approach uses a Nitinol tube as the backbone, which can be slid in and out of the soft robot body to achieve robot pose or stiffness modulation. To validate the proposed idea, we fabricated a tendon-driven concentric tube (TDCT) soft robot and developed the model based on Cosserat rod theory. The model is validated in different scenarios by varying the joint-space tendon input and task-space external contact force. Experimental results indicate that the model is capable of estimating the shape of the TDCT soft robot with an average root-mean-square error (RMSE) of 0.90 (0.56% of total length) mm and average tip error of 1.49 (0.93% of total length) mm. Simulation studies demonstrate that the Nitinol backbone insertion can enhance the kinematic workspace and reduce the compliance of the TDCT soft robot by 57.7%. Two case studies (object manipulation and soft laparoscopic photodynamic therapy) are presented to demonstrate the potential application of the proposed design. 

Powered knee exoskeletons have shown potential for mobility restoration and power augmentation. However, the benefits of exoskeletons are partially offset by some design challenges that still limit their positive effects on people. Among them, joint misalignment is a critical aspect mostly because the human knee joint movement is not a fixed-axis rotation. In addition, remarkable mass and stiffness are also limitations. Aiming to minimize joint misalignment, this paper proposes a bio-inspired knee exoskeleton with a joint design that mimics the human knee joint. Moreover, to accomplish a lightweight and high compliance design, a high stiffness cable-tension amplification mechanism is leveraged. Simulation results indicate our design can reduce 49.3 and 71.9% maximum total misalignment for walking and deep squatting activities, respectively. Experiments indicate that the exoskeleton has high compliance (0.4 and 0.1 Nm backdrive torque under unpowered and zero-torque modes, respectively), high control bandwidth (44 Hz), and high control accuracy (1.1 Nm root mean square tracking error, corresponding to 7.3% of the peak torque). This work demonstrates performance improvement compared with state-of-the-art exoskeletons. 

Nonionic hydrogels are of particular interest for long‐term therapeutic implantation due to their minimal immunogenicity relative to their charged counterparts. However, in situ formation of nonionic supramolecular hydrogels under physiological conditions has been a challenging task. In this context, we report on our discovery of salt‐triggered hydrogelation of nonionic supramolecular polymers (SPs) formed by self‐assembling prodrug hydrogelators (SAPHs) through the Hofmeister effect. The designed SAPHs consist of two SN‐38 units, which is an active metabolite of the anticancer drug irinotecan, and a short peptide grafted with two or four oligoethylene glycol (OEG) segments. Upon self‐assembly in water, the resultant nonionic SPs can be triggered to gel upon addition of phosphate salts. Our¹H NMR studies revealed that the added phosphates led to a change in the chemical shift of the methylene protons, suggestive of a disruption of the water‐ether hydrogen bonds and consequent reorganization of the hydration shell surrounding the SPs. This deshielding effect, commensurate with the amount of salt added, likely promoted associative interactions among the SAPH filaments to percolate into a 3D network. The formed hydrogels exhibited a sustained release profile of SN‐38 hydrogelator that acted potently against cancer cells.

 

Nonionic hydrogels are of particular interest for long‐term therapeutic implantation due to their minimal immunogenicity relative to their charged counterparts. However, in situ formation of nonionic supramolecular hydrogels under physiological conditions has been a challenging task. In this context, we report on our discovery of salt‐triggered hydrogelation of nonionic supramolecular polymers (SPs) formed by self‐assembling prodrug hydrogelators (SAPHs) through the Hofmeister effect. The designed SAPHs consist of two SN‐38 units, which is an active metabolite of the anticancer drug irinotecan, and a short peptide grafted with two or four oligoethylene glycol (OEG) segments. Upon self‐assembly in water, the resultant nonionic SPs can be triggered to gel upon addition of phosphate salts. Our¹H NMR studies revealed that the added phosphates led to a change in the chemical shift of the methylene protons, suggestive of a disruption of the water‐ether hydrogen bonds and consequent reorganization of the hydration shell surrounding the SPs. This deshielding effect, commensurate with the amount of salt added, likely promoted associative interactions among the SAPH filaments to percolate into a 3D network. The formed hydrogels exhibited a sustained release profile of SN‐38 hydrogelator that acted potently against cancer cells.

 

We present a simulation framework for multibody dynamics via a universal variational integration. Our method naturally supports mixed rigid-deformables and mixed codimensional geometries, while providing guaranteed numerical convergence and accurate resolution of contact, friction, and a wide range of articulation constraints. We unify (1) the treatment of simulation degrees of freedom for rigid and soft bodies by formulating them both in terms of Lagrangian nodal displacements, (2) the handling of general linear equality joint constraints through an efficient change-of-variable strategy, (3) the enforcement of nonlinear articulation constraints based on novel distance potential energies, (4) the resolution of frictional contact between mixed dimensions and bodies with a variational Incremental Potential Contact formulation, and (5) the modeling of generalized restitution through semi-implicit Rayleigh damping. We conduct extensive unit tests and benchmark studies to demonstrate the efficacy of our method.