Robust control barrier function-based shared control for cognitive-physical human-robot collaboration

Neural Adaptive Admittance Control With Guaranteed Performance for Physical Human–Robot Interaction

Physical human-robot interaction (pHRI) offers considerable potential for improving task efficiency and alleviating operator workload. Nevertheless, the intrinsic variability of human motion intention (HMI) and robot model uncertainties pose substantial challenges to achieving accurate coordinated control. To address these issues, this paper proposes a guaranteed-performance neural adaptive admittance control framework. First, the damping coefficient is dynamically tuned using real-time interaction force feedback, while a neural network (NN) is employed to estimate HMI-induced uncertainties in the coupled human-robot system. These two components are then integrated into the admittance model to construct a high-level interaction strategy that generates compliant reference trajectories for smooth and stable collaboration. Subsequently, low-level motion control with error transformation is developed to enforce prescribed output constraints, thereby ensuring unified regulation of transient and steady-state performance. Moreover, another NN is introduced to approximate the lumped robot dynamics for improved tracking accuracy. Finally, the effectiveness and superiority of the proposed method are validated through trajectory tracking, circle drawing, and obstacle avoidance tasks. Note to Practitioners—This paper focuses on developing an active interaction control approach that enables high-performance tracking for robots subject to model uncertainties while providing high-quality assistance to operators with unknown motion intention. The proposed framework is well-suited to industrial applications such as human-robot cooperative assembly and co-transportation. By incorporating output-constraint-based neural adaptive admittance control, safe, reliable, and compliant physical interaction can be achieved. Consequently, the controller supports further extension to medical rehabilitation and exoskeleton systems, demonstrating broad promise across a wide range of interaction-intensive scenarios.

Adaptive Safety Filter for Refining Manipulator Control in Shared Human-Robot Spaces*

Human–Robot Shared Workspace Safety Enhancement Using Predictive Control

The concept of human-robot collaboration (HRC) is becoming a significant part of the contemporary industry as it aims at enhancing productivity, flexibility, and ergonomics. As opposed to conventional industrial robotics, where keeping physical distance between humans and robots guarantees safety, collaborative robots (cobots) work in the same work areas whereby humans and robots are in close and simultaneous contact. The above paradigm shift brings about huge safety issues because of unpredictable human behaviors, changing environments as well as balancing between safety and operation. Traditional reactive safety measures, e.g., emergency stops and fixed safety zones, tend to cause unjustifiable down-time and low productivity. In this paper, the paper builds up detailed research on improving the safety of the shared workspace of human and robot with predictive control methods the author emphasizes the idea of Model Predictive Control (MPC) and its variations. Predictive control provides the opportunity to predict upcoming human behaviors and environmental variations enabling the adjustment of the robot paths, its speed, and interaction forces in advance. This framework is suggested and combines human motion prediction, dynamic safety constraints, and optimal control formulation to realize safe, smooth, and efficient human-robot collaboration. An elaborate system architecture is created which includes sensor fusion, real time prediction models and constrained optimization. The mathematical formulations of the predictive control problem are offered, including cost functions and safety constraints that meet the international safety standards. The feasibility of the suggested solution is assessed by the means of simulation-based scenarios of the most common industrial processes, including cooperative assembly and handling of materials. Findings indicate that the involved safety measures, the lower probability of a collision, the ease of control of the behavior of a robot, as well as greater functionality in its tasks, are significantly better than those of the traditional reactive control schemes. The article is informative and gives a systematic guide to scholars and developers intending to implement predictive safety control in the collaborative robot system and the future research directions to adhere to robust, explainable, and standardized human-robot safety solutions.

Robustness-Aware Physical AI for Multi-Functional Humanoid Robot Team Concurrency Control Under Imperfect Digital Twin Information

Humanoid robots are emerging as flexible robotic resources for autonomous manufacturing systems, where different types of tasks must be assigned to suitable robots while shared production resources are coordinated effectively. However, realistic manufacturing environments involve dynamic task arrivals, event-driven priority changes, heterogeneous robot capabilities, and shared-resource contention. In addition, digital twin-based scheduling may rely on state information that is delayed or uncertain, which can reduce the reliability of scheduling decisions. To address this issue, this paper extends the previously proposed deep reinforcement learning-based concurrency control (DRLCC) framework for robustness-aware scheduling and shared-resource control of a multi-functional humanoid robot team. The extended framework integrates capability-aware task assignment, feasibility-based action masking, and priority ceiling protocol (PCP)-based shared-resource coordination under delayed and uncertain digital twin observations. The framework is evaluated in a humanoid-based autonomous manufacturing scenario using performance indicators including task completion, urgent-task delay, resource contention, humanoid utilization, and robustness degradation under imperfect state feedback. Compared with the greedy ceiling-based baseline, DRLCC reduces high-priority task delay by approximately 10.9%, priority inversions by 42.1%, average waiting time by 73.8%, average block count by 74.0%, and temporary infeasible events by 73.5%, while maintaining comparable humanoid utilization. The robustness analysis further shows that overall task-completion performance remains stable under imperfect digital twin feedback, although coordination-level metrics are more sensitive to observation uncertainty and delay. These results suggest that the extended DRLCC framework can support robustness-aware humanoid robot team scheduling in autonomous manufacturing environments where digital twin observations are delayed or uncertain.

Principled Authority Switching for Shared Autonomy in Human-Robot Teams

Shared autonomy requires principled mechanisms for allocating and transferring control between a human and an autonomous agent. Existing approaches often rely on blending control inputs or heuristic switching rules, which lack theoretical guarantees and fail to account for the dynamics of authority transfer. This paper develops a cooperative game-theoretic framework for authority switching in shared autonomy. We formulate the control switching problem as an identical-interest dynamic game in which authority transitions are embedded into the system dynamics, yielding optimal switching policies rather than ad hoc rules. We establish the existence and characterization of team-optimal policies in pure strategies under stochastic human override, accounting for asymmetric authority where humans retain override capability. For linear-quadratic systems, we derive closed-form recursions for the optimal switching policies and value functions, enabling efficient computation independent of the continuous state. We validate the framework on scalar and multi-dimensional linear systems, demonstrating how optimal switching adapts to varying system dynamics, cost structures, and override probabilities. The results reveal fundamental trade-offs between human adaptability and autonomous efficiency, illustrating the practical benefits of grounding shared autonomy in cooperative game theory.

URF: A Unified Robot Control-Policy Framework for Stable Contact Aware Manipulation

Learning-based manipulation policies usually predict robot actions from sensory observations and leave their execution to a separate low-level controller. In rigid contact, this separation can be problematic: the same motion to a virtual target or compliant motion command can lead to unstable contact, tracking error, excessive loading, or tool damage, depending on the low-level controller. In this paper, we propose a \textit{Unified Robot Control-Policy Framework} (URF), which connects compliant action prediction with unified impedance-admittance control. Given multimodal observations, URF predicts a virtual target, a stiffness matrix, and an impedance-admittance switch ratio. The switch ratio determines when the controller should behave more like admittance control for accurate motion tracking and when it should move toward impedance control for safer rigid contact. Because demonstration data do not provide ground-truth environment stiffness, we construct switch-ratio labels from measured contact forces and use them to supervise controller-mode prediction. Across box-flipping and line-pressing tasks, URF achieves higher task success rates while reducing failure modes observed with admittance-only execution, including rapid force buildup, large force oscillations, tool breakage, and robot safety stops. These results suggest that contact-aware policies benefit from predicting not only compliant actions but also the controller behavior used to execute them. Project page: https://jiyou384.github.io/urf_project_page/