Object pose estimation has extensive applications in various industrial scenarios. However, the heavy reliance on dense 6-D annotation and textured object models has become a significant obstacle to the widespread industrial application of 6-D object pose estimation methods. In this work, we presentSS-Pose, a self-supervised learning framework for estimating 6-D object poses without annotated 6-D data and textured model.SS-Poseproposes thecoordinate system datum reinitializerstage to dynamically establish a sequence-level pose representation datum, and thetemporal–spatial constraint resolvermodule to obtain the self-supervised learning target through interframe constraints. We introduce a one-shotcross-coordinate transformationthat establishes the relationship between the 6-D representation and the object poses, which can be further utilized in real-world tasks. We evaluated the proposedSS-Poseon the challenging YCB-Video dataset and texture-less T-LESS dataset. Our approach achieves competitive performance with significantly lower data dependency, making it suitable for visual perception in industrial applications.

Upper limb rehabilitation robots are mainly used as a physical therapy method to passively or actively train the affected side. However, they are rarely implemented in accordance with the occupational therapy theory, which is dedicated to improving the sensorimotor coordination of hemiplegic patients by considering both healthy and affected limbs. To realize the occupational therapy concept in robot-assisted upper limb rehabilitation, we propose a new human-robot collaboration framework for hemiplegic patients that integrates healthy/affected limbs and robot. The strategy aims at achieving patient-specific movement capabilities and improving the participation of the affected limb during rehabilitation. To accomplish this task, we have addressed two essential issues: accurate motion estimation of the healthy limb and the rehabilitation trajectory learning technique. The posture estimation is achieved by introducing the calibration model to reduce static and time dependent errors during the measurement. We also introduce a force term to the conventional imitation learning method to improve the adaptability in integrating the affected side in cooperation with the robot. Various experiments have been conducted to validate the feasibility and effectiveness of our proposed dual-arm collaboration strategy.

Lower Limb Exoskeletons (LLE) have received considerable interest in strength augmentation, rehabilitation, and walking assistance scenarios. For strength augmentation, LLE is expected to have the capability of reducing metabolic energy. However, the energy for adjusting Center of Gravity (CoG) is a main part of the total energy consumed during walking. This paper proposes a novel Human-exoskeleton Cooperative Balance (HCB) strategy which gives assistive torques balance ability and combine with the direction selected by the pilot to achieve balance walking of human-exoskeleton systems. In which, a Dynamic Torque Primitive Model (DTPM) is designed to plan a bionic assistive torque, and the balance parameters obtained by an Inverted Pendulum Model (IPM) is superimposed on it. Finally, the performance improved by the HCB strategy can break the limitation of traditional strategies and substantially increase the efficiency of assistance. We demonstrated the effectiveness of the proposed HCB strategy on the HUman-powered Augmentation Lower EXoskeleton (HUALEX) system. Experimental results indicate that the proposed HCB is more efficient than traditional strategies.

Estimating the center of mass (CoM) is essential for both gait planning and controlling of lower limb exoskeletons. Different from CoM estimation in human and humanoid robots, a critical issue in human-exoskeleton systems pis how to describe the effect of physical human-exoskeleton interactions in estimating the CoM of lower limb exoskeletons. This paper presents a novel center of mass estimation method Physically Coupled Serial Chain (PCSC) for human-coupled lower limb exoskeleton systems. Different from traditional serial chain methods, the proposed PCSC involves physical human-exoskeleton models to describe physical interactions between the pilot and the lower limb exoskeleton. We demonstrated the effectiveness of proposed PCSC model in the AIDER lower limb exoskeleton system. Experimental results indicate that the proposed PCSC model is more accuracy than traditional serial chain methods.

6D object pose estimation is an essential task in vision-based robotic grasping and manipulation. Prior works extract spatial features by fusing the RGB image and depth without considering the temporal motion information, limiting their performance in heavy occlusion robotic grasping scenarios. In this paper, we present an end-to-end model named TemporalFusion, which integrates the temporal motion information from RGB-D images for 6D object pose estimation. The core of proposed TemporalFusion model is to embed and fuse the temporal motion information from multi-frame RGB-D sequences, which could handle heavy occlusion in robotic grasping tasks. Furthermore, the proposed deep model can also obtain stable pose sequences, which is essential for real-time robotic grasping tasks. We evaluated the proposed method in the YCB-Video dataset, and experimental results show our model outperforms state-of-the-art approaches. Our code is available at https://github.com/mufengjun260/TemporalFusion21.

Lower Limb Exoskeletons (LLEs) are promising in gait rehabilitation for stroke survivors. In gait training of post-stroke patients with LLEs, one of the main challenges is how to generate appropriate gait patterns from the sound leg to the paretic leg for different patients with varying walking speeds. In this paper, we proposed a Synergetic Gait Prediction (SGP) model for rehabilitation LLEs with post-stroke patients, which can generate adaptive synergetic gait patterns for different patients with varying walking speeds. The proposed SGP model is based on Sequence-to-Sequence (Seq2Seq) neural networks with temporal attention mechanisms. In the training procedure of the proposed SGP model, a gait database with collected gait patterns from healthy subjects is employed to learn the parameters of SGP model. The SGP model takes current joint angles from the sound leg and a segment of observed history joint angles from both legs as input and predicts the future joint angles for the paretic leg. We compared the effectiveness of the SGP model with the Long Short Term Memory (LSTM) model, experimental results indicate that SGP model can generate synergetic gait patterns for different subjects via varying walking speeds with less prediction error.

In recent years, lower limb exoskeletons have gained considerable interest in applications of walking assistance for paraplegic patients. In daily lives, the exoskeleton should have the ability to help the patients to walk over different terrains. For sloped terrains, how to plan the stepping locations on slopes with different gradients and generate stable human-like gaits for patients is a critical issue. In this article, we proposed a slope gradient estimator (SGE) based on the sensor data fusion of the exoskeleton and combined SGE with the capture point theory and dynamic movement primitives (DMP) to construct an adaptive gait planning approach for slopes. After learning from demonstrated gaits sampled from healthy subjects, adaptive gait trajectories can be reproduced online to adapt to slopes with different gradients. The efficiency of the proposed approach was demonstrated on an exoskeleton system named AIDER. Experimental results indicate that the proposed approach can endow exoskeletons with the ability to generate appropriate gaits for different slopes. Note to Practitioners-For lower limb exoskeletons, it is a vital problem to plan the gait for sloped terrains. Considering different gradients among slopes, fixed predefined gait planning cannot cover all cases; thus, a slope gradient adaptive gait planning approach is necessary. The slope gradient estimator proposed in this article provides a possible slope gradient estimation method for exoskeletons or humanoid bipedal robots; it is easy to estimate the slope gradient only based on the local sensor data of the robot. The proposed dynamic gait generator provides lower limb exoskeletons and humanoid bipedal robots a possible adaptive gait planning framework and some flexibility for different slopes. The proposed approach may inspire more extended gait planning strategies for other terrains, such as stairs.

Lower-limb exoskeleton has gained considerable interests in walking assistance applications for paraplegic patients. In walking assistance of paraplegic patients, the exoskeleton should have the ability to help patients to walk over different terrains in the daily life, such as slope terrains. One critical issue is how to plan the stepping locations on slopes with different gradients, and generate stable and human-like gaits for patients. This paper proposed an adaptive gait planning approach which can generate gait trajectories adapt to slopes with different gradients for lower-limb walking assistance exoskeletons. We modeled the human-exoskeleton system as a 2D Linear Inverted Pendulum Model (2D-LIPM) with an external force in the two-dimensional sagittal plane, and proposed a Dynamic Gait Generator (DGG) based on an extension of the conventional Capture Point (CP) theory and Dynamic Movement Primitives (DMPs). The proposed approach can dynamically generate reference foot locations for each step on slopes, and human-like adaptive gait trajectories can be reproduced after the learning from demonstrated trajectories that sampled from level ground walking of normal healthy human. We demonstrated the efficiency of the proposed approach on both the Gazebo simulation platform and an exoskeleton named AIDER. Experimental results indicate that the proposed approach is able to provide the ability for exoskeletons to generate appropriate gaits adapt to slopes with different gradients.

Human-powered lower exoskeletons have received considerable interests from both academia and industry over the past decades, and encountered increasing applications in human locomotion assistance and strength augmentation. One of the most important aspects in those applications is to achieve robust control of lower exoskeletons, which, in the first place, requires the proactive modeling of human movement trajectories through physical human-robot interaction (pHRI). As a powerful representative tool for motion trajectories, dynamic movement primitives (DMP) have been used to model human movement trajectories. However, canonical DMP only offers a general representation of human movement trajectory and may neglects the interactive term, therefore it cannot be directly applied to lower exoskeletons which need to track human joint trajectories online, because different pilots have different trajectories and even same pilot might change his/her motion during walking. This paper presents a novel coupled cooperative primitive (CCP) strategy, which aims at modeling the motion trajectories online. Besides maintaining canonical motion primitives, we model the interaction term between the pilot and exoskeletons through impedance models, and propose a reinforcement learning method based on policy improvement and path integrals (PI2) to learn the parameters online. Experimental results on both a single degree-of-freedom platform and a HUman-powered Augmentation Lower EXoskeleton (HUALEX) system demonstrate the advantages of our proposed CCP scheme.

The current paper presents a wearable, adaptive and jointless hand exoskeleton to assist tendon therapy exercises. Since human fingers are distributed in a crosswise narrow space, it may cause many problems in size and flexibility if we apply traditional rigid structure based on pin joints. Besides, the distance between joints in human fingers varies from person to person, which means that the exoskeleton has to be tailor-made for each patient. However, a jointless structure with no pin joint can be very soft and ignores the diversity of the distance between human fingers. The proposed exoskeleton is developed to assist adduction and abduction motion of fingers. Smart Dual-Motor System with tension response is used actuate the exoskeleton. Based on kinematic models of the proposed device, assessment of the range of movement (ROM) can be made. The software is based on pressure-torque feedback control logic. The pressure sensors and tensiometers collect data and store it into several buffer for further processing with FIR filters. A finite state machine (FSM) is integrated to support the sequential logic.

Lower exoskeleton has gained considerable interests in walking assistance applications for both paraplegia and hemiplegia patients. In walking assistance of hemiplegia patients, the exoskeleton should have the ability to control the affected leg to follow the unaffected leg's motion naturally. One critical issue of walking assistance for hemiplegia patients is how to adapt the controller of both lower limbs with different patients. This paper presents a novel learning-based walking assistance control strategy for lower exoskeleton with hemiplegia patients. In the proposed control strategy, we modeled the control system of lower exoskeleton with hemiplegia patient as a Leader-Follower Multi-Agent System (LF -MAS). In order to adapt different patients with different conditions, reinforcement learning framework is utilized to adapt controllers online. In reinforcement learning framework with LF-MAS, we employed a Policy Iteration Adaptive Dynamic Programming (PI-ADP) algorithm, which aims to achieve better tracking control performance for lower exoskeleton with hemiplegia patient. We demonstrate the efficiency of proposed learning-based walking assistance control strategy in an exoskeleton system with healthy subjects who simulate hemiplegia patients. Experimental results indicate that the proposed control strategy can adapt different pilots with good tracking performance.