physical human-robot collaboration (pHRC) requires strict safety and efficiency guarantees, imposing heightened demands on accurate human intent estimation and adaptive control in a stable manner. To address these challenges, we propose a novel two-loop adaptive passive control framework guided by multistep human intent estimation to reduce human-robot disagreement and improve robot assistance level, facilitating safety-aware efficient pHRC. In the framework, outer loop's intent estimation guides the inner loop's adaptive passive controller, ensuring real-time robot behavior adjustment based on multistep intention. Specifically, the outer loop incorporates a transformer-based human intent estimator (THIE) that integrates the Transformer with a conditional variational autoencoder (CVAE) for multistep predictions, accurately estimating motion and force to guide the robot. The inner loop incorporates a goal-oriented reinforcement learning (GoRL)-based adaptive impedance control, which constructs multistep rewards based on prediction and probability from THIE to adjust impedance parameters, thereby balancing disagreement and assistance, and promoting locally optimal robot behaviors. Furthermore, an energy tank-based passive model predictive control (ET-PMPC) is employed to limit robot stored energy, avoiding the impact of variable impedance on safety. Experiments validate that our framework outperforms state-of-the-art (SOTA) methods, significantly improving intent estimation accuracy, robot assistance level, and safety, highlighting its potential to advance pHRC.

In physical Human-Robot Collaboration (pHRC), accurate human intent estimation and rational human-robot role allocation are crucial for safe and efficient assistance. Existing methods that rely on short-term motion data for intention estimation lack multi-step prediction capabilities, hindering their ability to sense intent changes and adjust human-robot assignments autonomously, resulting in potential discrepancies. To address these issues, we propose a Dual Transformer-based Robot Trajectron (DTRT) featuring a hierarchical architecture, which harnesses human-guided motion and force data to rapidly capture human intent changes, enabling accurate trajectory predictions and dynamic robot behavior adjustments for effective collaboration. Specifically, human intent estimation in DTRT uses two Transformer-based Conditional Variational Autoencoders (CVAEs), incorporating robot motion data in obstacle-free case with human-guided trajectory and force for obstacle avoidance. Additionally, Differential Cooperative Game Theory (DCGT) is employed to synthesize predictions based on human-applied forces, ensuring robot behavior align with human intention. Compared to state-of-the-art (SOTA) methods, DTRT incorporates human dynamics into long-term prediction, providing an accurate understanding of intention and enabling rational role allocation, achieving robot autonomy and maneuverability. Experiments demonstrate DTRT's accurate intent estimation and superior collaboration performance.

In Human-Robot Collaboration (HRC), which encompasses physical interaction and remote cooperation, accurate estimation of human intentions and seamless switching of collaboration modes to adjust robot behavior remain paramount challenges. To address these issues, we propose an Intent-Driven Adaptive Generalized Collaboration (IDAGC) framework that leverages multimodal data and human intent estimation to facilitate adaptive policy learning across multi-tasks in diverse scenarios, thereby facilitating autonomous inference of collaboration modes and dynamic adjustment of robotic actions. This framework overcomes the limitations of existing HRC methods, which are typically restricted to a single collaboration mode and lack the capacity to identify and transition between diverse states. Central to our framework is a predictive model that captures the interdependencies among vision, language, force, and robot state data to accurately recognize human intentions with a Conditional Variational Autoencoder (CVAE) and automatically switch collaboration modes. By employing dedicated encoders for each modality and integrating extracted features through a Transformer decoder, the framework efficiently learns multi-task policies, while force data optimizes compliance control and intent estimation accuracy during physical interactions. Experiments highlights our framework’s practical potential to advance the comprehensive development of HRC.

Enabling robots with uncertain dynamics to perform human-like adaptive operations in unknown environments remains a significant challenge in robotics research. Drawing inspiration from the dynamic modification of human arm muscles, we propose an innovative adaptive optimal control framework to address this issue. The framework integrates a variable optimal impedance adaptation (VOIA) method and an adaptive bias broad fuzzy neural network (ABBFNN) controller, facilitating adaptive manipulation behaviors in robot-environment interaction tasks. It can adaptively learn the impedance gain of unknown environment in the presence of uncertain robot dynamic model based on different task properties, simultaneously keeping the tracking error and interaction force optimized and minimized. The ABBFNN controller combines adaptive node increments to approximate uncertain dynamic model and introduces additional global bias and adaptive gain adjustment to improve the approximation accuracy and the rate of convergence significantly. VOIA seamlessly integrates a finely tuned proportional-integral-derivative (PID) variable target stiffness and an impact compensator, ensuring accurate responses to varying environmental conditions and improved disturbance rejection. Moreover, a momentum-based force observer is utilized within the framework for interaction force estimation, eliminating the need for force sensors and simplifying the system. Simulations and experiments validate the effectiveness and practicality of the proposed optimal interaction control framework, demonstrating its potential to propel robots toward interactions with unknown environments.

Point cloud primitive segmentation aims to segment the surface point cloud into various geometric types of primitives, which plays a vital role in robot operation and industrial automation. However, differences in object structures and shapes across industrial datasets create domain shift issues, compounded by privacy concerns preventing dataset sharing. To address these challenges, we propose a novel source-free domain adaptation method for point cloud primitive segmentation, which follows the popular pseudo-label based self-training framework. Unlike previous works using single-model uncertainty to refine pseudo labels, our method leverages multi-confidence, including transformation consistency, task confidence, and geometric saliency to provide more informative guidance. Specifically, the transformation consistency is first utilized to vote pseudo-labels and task confidences. Furthermore, to filter out high-confident noises and obtain more reliable pseudo-labels, we investigate the geometric curvature properties of primitives and propose a geometric saliency guided dynamic prototype matching and label graph aggregation strategies for pseudo-label reassignment with different task confidence. For this novel task, we construct several datasets and verify the effectiveness of the proposed methods through a series of experiments.

This paper addresses a long-standing yet well-documented open problem on trajectory tracking control of manipulators, which is simultaneously affected by unknown kinematics and uncertain dynamics. A theoretical framework for implementing exponential tracking control is established by unifying two novel controllers, i.e., the Jacobian matrix adaption (JMA) controller and the observer-based estimation law, into an integrated control system. The proposed JMA controller converts internal, implicit and immeasurable model information to external, explicit and measurable input-output information for adaptive learning of unknown kinematics. The proposed observer-based estimation law can guarantee the global exponential stability of the estimation error for accurately estimating uncertain dynamics. In addition, the inherent measurement noise, hard nonlinearity, and limited sampling period lead to chattering phenomenon and accumulated errors occur during convergence process. Hence, an improved simple model-free adaptive sliding mode control (ASMC) scheme is proposed to compensate these limitations, which has fast adaptability and powerful tracking and chattering suppression capabilities. It is theoretically proved that the integrated control system is globally exponentially stable. Simulation, experiments and comparison verify the convergence performance of the proposed integrated control system.Note to Practitioners—Despite the enormous advantages provided by advanced robotic manipulators, developing effective tracking control schemes remains a challenging problem Unfortunately, however, the assumption of precise kinematic and dynamic parameters during tracking control is practically unrealistic due to the absence of precise parameters and measurements of the interaction between robotic manipulators and external environment. This uncertainty will reduce the control performance of manipulators, such as accuracy and repeatability. It is of practical significance to further solve the tracking control of manipulators while considering both the uncertain kinematics and the uncertain dynamics. Therefore, this paper addresses the tracking control problem of manipulators, which is simultaneously affected by unknown kinematics and uncertain dynamics. By unifying the two novel controllers into an integrated control system, a theoretical framework for implementing exponential tracking control is established. In addition, an improved scheme is proposed to compensate chattering phenomenon and accumulated errors during the convergence process. The superior convergence performance of the integrated control system are verified by the simulation and experiments.