Exoskeletons are being adopted as assistive devices in industries such as manufacturing, logistics, and construction, aimed at reducing musculoskeletal loads in workers. Presently, their design process assumes the user to be quasi-static, optimizing the design parameters for reduction of human joint torques followed by fine-tuning through usability studies and physical prototyping. We present a method for optimizing passive exoskeleton designs before the physical prototyping stage for muscle effort reduction in dynamic tasks such as arm reaching and walking. We employ fast MuJoCo-based simulations of human biomechanics to compute the joint torques, muscle forces and muscle activations while executing task trajectories using pre-trained reinforcement learning models from the literature. We train another set of reinforcement learning models that minimize joint torques and muscle effort rates by varying the exoskeleton's design parameters online during the task motions. Baselines for comparison include the default designs of shoulder and walking assist exoskeletons from the literature, and designs obtained through conventional optimization techniques. In terms of muscle effort rates, the RL-based designs improved upon these baselines by an average of 3.42% and 1.96% respectively in the arm reaching task, and 6.28% and 5.81% in the walking task. Our method can be adapted to evaluate exoskeletons in real-time through motion capture, and for muscle-aware online control of powered exoskeletons.

Humanoid robots are increasingly being deployed in industrial scenarios. However, creating controllers for their locomotion remains challenging, especially in unstructured environments. In this work, we leverage the morphological symmetry about the sagittal plane in high-dimensional humanoid agents to enhance locomotion learning. We incorporate group-equivariant neural networks in reinforcement learning (RL) through Proximal Policy Optimization (PPO). Using Equivariant Multi-Layer Perceptrons (EMLP), we aim to improve sample efficiency and RL training stability. Our experiments with the 21-DoF Unitree H1 humanoid suggest that while EMLP combined with PPO improves sample efficiency, vanilla PPO achieves marginally higher performance in terms of gait quality and biomechanical realism. For assessment, we propose a suite of human-inspired biomechanical metrics, such as joint trajectory deviation, gait symmetry, phase consistency, energy efficiency, and motion smoothness—comparing learned policies against human motion capture (MoCap) data. These metrics are used for quantitatively evaluating the similarity between human gait and reinforcement learning-based humanoid locomotion. Surprisingly, despite the theoretical benefits of equivariance, our findings suggest that excessive symmetry constraints may limit expressivity and impede the emergence of human-like locomotion in complex agents. This study provides key insights into the trade-offs of incorporating symmetry priors in deep reinforcement learning for humanoid control.

Grasping and manipulation remain fundamental challenges in the effective deployment of robotic systems in real-world applications. In retail and supermarket scenarios, soft robotic grippers enable safe and efficient material handling. However, existing grasp planners are designed for rigid or suction-based grippers. Soft grasping is more challenging in terms of planning, estimation and sensing due to deflections in the gripper material on contact with the target. We present a system for soft robotic grasping using a custom gripper with a reconfigurable wrist that leverages reinforcement learning to augment existing vision-based techniques to adapt to the target object’s geometry. This system includes a hidden superquadrics module to guide the adaptation of the gripper’s palm configuration. We evaluate this system in a PyBullet simulation environment and compare it with a baseline synergy-based grasp strategy. Ongoing and future work involves transferring this planner to our physical robotic platform and evaluation in retail stores.

Managing shelves in retail stores includes re-stocking, rearrangement and replenishment of products. As these are some of the most labor-intensive activities, there has been widespread demand from retailers for automation in this domain. However, major challenges still remain in perception, navigation and manipulation while implementing an autonomous robotic system for this purpose. We present a system aimed at addressing some of these challenges through novel approaches. In terms of perception, we have developed a transformer-based local anomaly detection algorithm that can identify misplaced items without the need for a central database. Navigation of the omnidirectional mobile base is performed through stereo vision and LiDAR sensors. Finally, identifying grasping and manipulation as one of the key shortcomings of present robotic systems in this domain, we have developed a customized soft robotic gripper targeted at retail objects. It has compliant cable-driven fingers, and a palm configuration that can be adapted in real-time based on the target object's geometry. Coupled with a conventional two-fingered gripper in a dual-arm setup, this system is equipped to handle most objects encountered in a retail setting. We describe the underlying hardware and algorithms for each component of the system, evaluating their individual performance. We then evaluate the whole system in a mock retail setup, demonstrating promising results for autonomous management of shelves.

The swiftly expanding retail sector is increasingly adopting autonomous mobile robots empowered by artificial intelligence and machine learning algorithms to gain an edge in the competitive market. However, these autonomous robots encounter challenges in adapting to the dynamic nature of retail products, often struggling to operate autonomously in novel situations. In this study, we introduce an omni-directional dual-arm mobile robot specifically tailored for use in retail environments. Additionally, we propose a tele-operation method that enables shared control between the robot and a human operator. This approach utilizes a Virtual Reality (VR) motion capture system to capture the operator's commands, which are then transmitted to the robot located remotely in a retail setting. Furthermore, the robot is equipped with heterogeneous grippers on both manipulators, facilitating the handling of a wide range of items. We validate the efficacy of the proposed system through testing in a mockup of retail environment, demonstrating its ability to manipulate various commonly encountered retail items using both single and dual-arm coordinated manipulation techniques.

Tracking of inventory and rearrangement of mis-placed items are some of the most labor-intensive tasks in a retail environment. While there have been attempts at using vision-based techniques for these tasks, they mostly use planogram compliance for detection of any anomalies, a technique that has been found lacking in robustness and scalability. Moreover, existing systems rely on human intervention to perform corrective actions after detection. In this paper, we present Co-AD, a Concept-based Anomaly Detection approach using a Vision Transformer (ViT) that is able to flag misplaced objects without using a prior knowledge base such as a planogram. It uses an auto-encoder architecture followed by outlier detection in the latent space. Co-AD has a peak success rate of 89.90% on anomaly detection image sets of retail objects drawn from the RP2K dataset, compared to 80.81% on the best-performing baseline of a standard ViT auto-encoder. To demonstrate its utility, we describe a robotic mobile manipulation pipeline to autonomously correct the anomalies flagged by Co-AD. This work is ultimately aimed towards developing autonomous mobile robot solutions that reduce the need for human intervention in retail store management.

Teleoperated robots have enabled humans to manipulate objects in remote environments without requiring physical presence. In this paper we focus on teleoperation of a robotic arm with shared control between the robot and the operator. A model-mediated approach is used to compensate for delays in the communication channel. Position information of the operator's arm is captured and processed to compute the states of a motion prediction model before transmission over a network to be used on the robot's side, allowing for compensation of transmission delays. Model Predictive Control (MPC) and a novel goal prediction algorithm is used to follow the operator's intended motion while reducing the cognitive loads arising from collision avoidance and fine manipulation in the remote environment. We evaluate the proposed method against a baseline pure teleoperation condition with an inverse kinematic controller and observe that the proposed approach improves the overall teleoperation performance in terms of task completion time.

Passive exoskeletons are unpowered wearable robotic devices aimed at providing biomechanical assistance. They can be applied in industries such as manufacturing, construction and logistics to reduce repetitive stress injuries among workers. Their design process typically considers a static user, with muscle outputs computed later during dynamic tasks to evaluate performance. Attempting to reduce human muscle effort at this stage requires manual redesign. Instead, we propose a parameter optimization approach that minimizes muscle effort rates during realistic dynamic tasks in the design stage itself. We extract human kinematics in assembly tasks from an industry-oriented motion capture dataset, and compute the induced joint torques. Using a passive exoskeleton for shoulder joint gravity compensation from the literature as a baseline, we optimize its design parameters through a multi-objective Pareto Local Search, minimizing the muscle effort rates during these tasks. As the estimation of muscle outputs through biomechanical simulation techniques is computation-ally expensive, we train ensemble regression models for each muscle of interest during the task motions. These models serve as surrogates for the objective function in the design optimization procedure, speeding up search in the parameter space. The resulting exoskeleton with optimized design param-eters reduces estimated muscle effort rates by an average of 5.73% and peak of 35.1 % compared to default parameters, and an average of 14.5% and peak of 32.2% compared to not wearing an exoskeleton in overhead assembly tasks. A larger peak reduction compared to default parameters may be due to hindrance in motion caused by device. This approach may be adapted to other exoskeletons and applications, improving biomechanical assistance by design.

This paper presents the design of a wearable robotic forearm for close-range human-robot collaboration. The robot's function is to serve as a lightweight supernumerary third arm for shared workspace activities. We present a functional prototype resulting from an iterative design process including several user studies. An analysis of the robot's kinematics shows an increase in reachable workspace by 246 % compared to the natural human reach. The robot's degrees of freedom and range of motion support a variety of usage scenarios with the robot as a collaborative tool, including self-handovers, fetching objects while the human's hands are occupied, assisting human-human collaboration, and stabilizing an object. We analyze the bio-mechanical loads for these scenarios and find that the design is able to operate within human ergonomic wear limits. We then report on a pilot human-robot interaction study that indicates robot autonomy is more task-time efficient and preferred by users when compared to direct voice-control. These results suggest that the design presented here is a promising configuration for a lightweight wearable robotic augmentation device, and can serve as a basis for further research into human-wearable collaboration.

This paper presents the design of a wearable robotic forearm that provides the user with an assistive third hand, along with a study of interaction scenarios for the design. Technical advances in sensors, actuators, and materials have made wearable robots feasible for personal use, but the interaction with such robots has not been sufficiently studied. We describe the development of a working prototype along with three usability studies. In an online survey we find that respondents presented with images and descriptions of the device see its use mainly as a functional tool in professional and military contexts. A subsequent contextual inquiry among building construction workers reveals three themes for user needs: extending a worker's reach, enhancing their safety and comfort through bracing and stabilization, and reducing their cognitive load in repetitive tasks. A subsequent laboratry study in which participants wear a working prototype of the robot finds that they prioritize lowered weight and enhanced dexterity, seek adjustable autonomy and transparency of the robot's intent, and prefer a robot that looks distinct from a human arm. These studies inform design implications for further development of wearable robotic arms.