This paper explores a physical human-robot collaboration (pHRC) task involving the joint insertion of a board into a frame by a sightless robot and a human operator. While admittance control is commonly used in pHRC tasks, it can be challenging to measure the force/torque applied by the human for accurate human intent estimation, limiting the robot's ability to assist in the collaborative task. Other methods that attempt to solve pHRC tasks using reinforcement learning (RL) are also unsuitable for the board-insertion task due to its safety constraints and sparse rewards. Therefore, we propose a novel RL approach that utilizes a human-designed admittance controller to facilitate more active robot behavior and reduce human effort. Through simulation and real-world experiments, we demonstrate that our approach outperforms admittance control in terms of success rate and task completion time. Additionally, we observed a significant reduction in measured force/torque when using our proposed approach compared to admittance control. The video of the experiments is available at https://youtu.be/va07Gw6YIog.

--Precisely grasping an object is a challenging task due to pose uncertainties. Conventional methods have used cameras and fixtures to reduce object uncertainty. They are effective but require intensive preparation, such as designing jigs based on the object geometry and calibrating cameras with high-precision tools fabricated using lasers. In this study, we propose a method to reduce the uncertainty of the position and orientation of a grasped object without using a fixture or a camera. Our method is based on the concepts that the flat finger pads of a parallel gripper can reduce uncertainty along its opening/closing direction through flat surface contact. Three orthogonal grasps by parallel grippers with flat finger pads collectively constrain an object's position and orientation to a unique state. Guided by the concepts, we develop a regrasp planning and admittance control approach that sequentially finds and leverages three orthogonal grasps of two robotic arms to eliminate uncertainties in the object pose. We evaluated the proposed method on different initial object uncertainties and verified that the method have satisfactory repeatability accuracy. It outperforms an AR marker detection method implemented using cameras and laser jet printers under standard laboratory conditions. Significant challenge in robotic manipulation lies in addressing the uncertainties associated with object grasping. The uncertainties often arise from errors in environmental registration, inaccuracies in object pose recognition, and unbalanced contact during grasping that leads to pose deviations. The uncertainties can result in discrepancies between the actual and expected pose of objects or tools, potentially causing task failures.

This paper presents a novel method for assistive load carrying using quadruped robots. The controller uses proprioceptive sensor data to estimate external base wrench, that is used for precise control of the robot's acceleration during payload transport. The acceleration is controlled using a combination of admittance control and Control Barrier Function (CBF) based quadratic program (QP). The proposed controller rejects disturbances and maintains consistent performance under varying load conditions. Additionally, the built-in CBF guarantees collision avoidance with the collaborative agent in front of the robot. The efficacy of the overall controller is shown by its implementation on the physical hardware as well as numerical simulations. The proposed control framework aims to enhance the quadruped robot's ability to perform assistive tasks in various scenarios, from industrial applications to search and rescue operations.

The interest in Physical Human-Robot Interaction (pHRI) has significantly increased over the last two decades thanks to the availability of collaborative robots that guarantee user safety during force exchanges. For this reason, stability concerns have been addressed extensively in the literature while proposing new control schemes for pHRI applications. Because of the nonlinear nature of robots, stability analyses generally leverage passivity concepts. On the other hand, the proposed algorithms generally consider ideal models of robot manipulators. For this reason, the primary objective of this paper is to conduct a detailed analysis of the sources of instability for a class of pHRI control schemes, namely proxy-based constrained admittance controllers, by considering parasitic effects such as transmission elasticity, motor velocity saturation, and actuation delay. Next, a sensitivity analysis supported by experimental results is carried out, in order to identify how the control parameters affect the stability of the overall system. Finally, an adaptation technique for the proxy parameters is proposed with the goal of maximizing transparency in pHRI. The proposed adaptation method is validated through both simulations and experimental tests.

Physical human-robot interaction (pHRI) is widely needed in many fields, such as industrial manipulation, home services, and medical rehabilitation, and puts higher demands on the safety of robots. Due to the uncertainty of the working environment, the pHRI may receive unexpected impact interference, which affects the safety and smoothness of the task execution. The commonly used linear admittance control (L-AC) can cope well with high-frequency small-amplitude noise, but for medium-frequency high-intensity impact, the effect is not as good. Inspired by the solid-liquid phase change nature of shear-thickening fluid, we propose a Shear-thickening Fluid Control (SFC) that can achieve both an easy human-robot collaboration and resistance to impact interference. The SFC's stability, passivity, and phase trajectory are analyzed in detail, the frequency and time domain properties are quantified, and parameter constraints in discrete control and coupled stability conditions are provided. We conducted simulations to compare the frequency and time domain characteristics of L-AC, nonlinear admittance controller (N-AC), and SFC, and validated their dynamic properties. In real-world experiments, we compared the performance of L-AC, N-AC, and SFC in both fixed and mobile manipulators. L-AC exhibits weak resistance to impact. N-AC can resist moderate impacts but not high-intensity ones, and may exhibit self-excited oscillations. In contrast, SFC demonstrated superior impact resistance and maintained stable collaboration, enhancing comfort in cooperative water delivery tasks. Additionally, a case study was conducted in a factory setting, further affirming the SFC's capability in facilitating human-robot collaborative manipulation and underscoring its potential in industrial applications.

This paper delves into various robotic manipulation control methods designed for dynamic contact tooling operations on a robotic repair platform. The explored control strategies include hybrid position-force control, admittance control, bilateral telerobotic control, virtual fixture, and shared control. Each approach is elucidated and assessed in terms of its applicability and effectiveness for handling contact tooling tasks in real-world repair scenarios. The hybrid position-force controller is highlighted for its proficiency in executing precise force-required tasks, but it demands contingent on an accurate model of the environment and structured, static environment. In contrast, for unstructured environments, bilateral teleoperation control is investigated, revealing that the compliance with the remote robot controller is crucial for stable contact, albeit at the expense of reduced motion tracking performance. Moreover, advanced controllers for tooling manipulation tasks, such as virtual fixture and shared control approaches, are investigated for their potential applications.

Flexible object manipulation of paper and cloth is a major research challenge in robot manipulation. Although there have been efforts to develop hardware that enables specific actions and to realize a single action of paper folding using sim-to-real and learning, there have been few proposals for humanoid robots and systems that enable continuous, multi-step actions of flexible materials. Wrapping an object with paper and tape is more complex and diverse than traditional manipulation research due to the increased number of objects that need to be handled, as well as the three-dimensionality of the operation. In this research, necessary information is organized and coded based on the characteristics of each object handled in wrapping. We also generalize the hardware configuration, manipulation method, and recognition system that enable humanoid wrapping operations. The system will include manipulation with admittance control focusing on paper tension and state evaluation using point clouds to handle three-dimensional flexible objects. Finally, wrapping objects with different shapes is experimented with to show the generality and effectiveness of the proposed system.

Dual-arm mobile manipulators can transport and manipulate large-size objects with simple end-effectors. To interact with dynamic environments with strict safety and compliance requirements, achieving whole-body motion planning online while meeting various hard constraints for such highly redundant mobile manipulators poses a significant challenge. We tackle this challenge by presenting an efficient representation of whole-body motion trajectories within our bilevel model-based predictive control (MPC) framework. We utilize B\'ezier-curve parameterization to represent the optimized collision-free trajectories of two collaborating end-effectors in the first MPC, facilitating fast long-horizon object-oriented motion planning in SE(3) while considering approximated feasibility constraints. This approach is further applied to parameterize whole-body trajectories in the second MPC for whole-body motion generation with predictive admittance control in a relatively short horizon while satisfying whole-body hard constraints. This representation enables two MPCs with continuous properties, thereby avoiding inaccurate model-state transition and dense decision-variable settings in existing MPCs using the discretization method. It strengthens the online execution of the bilevel MPC framework in high-dimensional space and facilitates the generation of consistent commands for our hybrid position/velocity-controlled robot. The simulation comparisons and real-world experiments demonstrate the efficiency and robustness of this approach in various scenarios for static and dynamic obstacle avoidance, and compliant interaction control with the manipulated object and external disturbances.

Reaction force-aware control is essential for legged climbing robots to ensure a safer and more stable operation. This becomes particularly crucial when navigating steep terrain or operating in microgravity environments, where excessive reaction forces may result in the loss of foot contact with the ground, leading to potential falls or floating over in microgravity. Furthermore, such robots are often tasked with manipulation activities, exposing them to external forces in addition to those generated during locomotion. To effectively handle such disturbances while maintaining precise motion trajectory tracking, we propose a novel control scheme based on position-based impedance control, also known as admittance control. We validated this control method through simulation-based case studies by intentionally introducing continuous and impact interference forces to simulate scenarios such as object manipulation or obstacle collisions. The results demonstrated a significant reduction in both the reaction force and joint torque when employing the proposed method.

Robot interaction control is often limited to low dynamics or low flexibility, depending on whether an active or passive approach is chosen. In this work, we introduce a hybrid control scheme that combines the advantages of active and passive interaction control. To accomplish this, we propose the design of a novel Active Remote Center of Compliance (ARCC), which is based on a passive and active element which can be used to directly control the interaction forces. We introduce surrogate models for a dynamic comparison against purely robot-based interaction schemes. In a comparative validation, ARCC drastically improves the interaction dynamics, leading to an increase in the motion bandwidth of up to 31 times. We introduce further our control approach as well as the integration in the robot controller. Finally, we analyze ARCC on different industrial benchmarks like peg-in-hole, top-hat rail assembly and contour following problems and compare it against the state of the art, to highlight the dynamic and flexibility. The proposed system is especially suited if the application requires a low cycle time combined with a sensitive manipulation.