Interactive articulated manipulation requires long-horizon, multi-step interactions with appliances while maintaining physical consistency. Existing vision-language and diffusion-based policies struggle to generalize across parts, instances, and categories. We first introduce ArtiBench, a five-level benchmark covering kitchen, storage, office, and tool environments. ArtiBench enables structured evaluation from cross-part and cross-instance variation to long-horizon multi-object tasks, revealing the core generalization challenges of articulated object manipulation. Building on this benchmark, we propose ArtiBrain, a modular framework that unifies high-level reasoning with adaptive low-level control. ArtiBrain uses a VLM-based Task Reasoner (GPT-4.1) to decompose and validate subgoals, and employs a Hybrid Controller that combines geometry-aware keyframe execution with affordance-guided diffusion for precise and interpretable manipulation. An Affordance Memory Bank continually accumulates successful execution episodes and propagates part-level actionable affordances to unseen articulated parts and configurations. Extensive experiments on ArtiBench show that our ArtiBrain significantly outperforms state-of-the-art multimodal and diffusion-based methods in robustness and generalization. Code and dataset will be released upon acceptance.

-- Robotic navigation in dense, cluttered environments such as agricultural canopies presents significant challenges due to physical and visual occlusion caused by leaves and branches. Traditional vision-based or model-dependent approaches often fail in these settings, where physical interaction without damaging foliage and branches is necessary to reach a target. We present a novel reactive controller that enables safe navigation for a robotic arm in a contact-rich, cluttered, deformable environment using end-effector position and real-time tactile feedback. Our proposed framework's interaction strategy is based on a trade-off between minimizing disturbance by maneuvering around obstacles and pushing through them to move towards the target. We show that over 35 trials in 3 experimental plant setups with an occluded target, the proposed controller successfully reached the target in all trials without breaking any branch and outperformed the state-of-the-art model-free controller in robustness and adaptability. This work lays the foundation for safe, adaptive interaction in cluttered, contact-rich deformable environments, enabling future agricultural tasks such as pruning and harvesting in plant canopies. Robots struggle to operate in an agricultural environment due to dense and unstructured clutter, such as overlapping leaves and branches [1]. This clutter creates both physical obstructions, which require robots to interact with or navigate around obstacles, and visual occlusions, which hinder perception and path planning toward targets like fruits. When navigating cluttered environments, there are generally three possible strategies: pushing through obstacles, navigating around them, or adaptively combining both [2].

We introduce a novel distributed source seeking framework, DIAS, designed for multi-robot systems in scenarios where the number of sources is unknown and potentially exceeds the number of robots. Traditional robotic source seeking methods typically focused on directing each robot to a specific strong source and may fall short in comprehensively identifying all potential sources. DIAS addresses this gap by introducing a hybrid controller that identifies the presence of sources and then alternates between exploration for data gathering and exploitation for guiding robots to identified sources. It further enhances search efficiency by dividing the environment into Voronoi cells and approximating source density functions based on Gaussian process regression. Additionally, DIAS can be integrated with existing source seeking algorithms. We compare DIAS with existing algorithms, including DoSS and GMES in simulated gas leakage scenarios where the number of sources outnumbers or is equal to the number of robots. The numerical results show that DIAS outperforms the baseline methods in both the efficiency of source identification by the robots and the accuracy of the estimated environmental density function.

-- This paper presents a unified algorithm for motion and force control for a six degree-of-freedom spatial manipulator . The motion-force contoller performs trajectory tracking, maneuvering the manipulator's end-effector through desired positions, orientations and rates. When contacting an obstacle or target object, the force module of the controller restricts the manipulator movements with a novel force exertion method, which prevents damage to the manipulator, end-effectors and objects during the contact or collision. The core strategy presented in this paper is to design the linear acceleration for the end-effector which ensures both trajectory tracking and restriction of any contact force at the end-effector . The design of the controller has been validated through numerical simulations and digital twin visualization. I. INTRODUCTION Robotic manipulators are used in various industries such as automotive and aerospace for a vast amount of applications. These common applications, such as material handling and assembly, require the end effector to follow the reference trajectories. In addition to trajectory tracking, a safe collaborative robot must control the force that the end-effector exerts upon contact with any obstacles during trajectory tracking. Specifically, the magnitude of the force that the robot exerts should be bounded by the maximum allowable force.

Abstract--This paper introduces a novel solution to the manual control challenge for indoor blimps. The problem's complexity arises from the conflicting demands of executing human commands while maintaining stability through automatic control for underactuated robots. To tackle this challenge, we introduced an assisted piloting hybrid controller with a preemptive mechanism, that seamlessly switches between executing human commands and activating automatic stabilization control. Our algorithm ensures that the automatic stabilization controller operates within the time delay between human observation and perception, providing assistance to the driver in a way that remains imperceptible. Unmanned aerial vehicles (UAVs) have become increasingly popular in various fields including military, agriculture, and significantly impact human perceptions of event causation transportation.

Soft robots have been leveraged in considerable areas like surgery, rehabilitation, and bionics due to their softness, flexibility, and safety. However, it is challenging to produce two same soft robots even with the same mold and manufacturing process owing to the complexity of soft materials. Meanwhile, widespread usage of a system requires the ability to replace inner components without highly affecting system performance, which is interchangeability. Due to the necessity of this property, a hybrid adaptive controller is introduced to achieve interchangeability from the perspective of control approaches. This method utilizes an offline-trained recurrent neural network controller to cope with the nonlinear and delayed response from soft robots. Furthermore, an online optimizing kinematics controller is applied to decrease the error caused by the above neural network controller. Soft pneumatic robots with different deformation properties but the same mold have been included for validation experiments. In the experiments, the systems with different actuation configurations and the different robots follow the desired trajectory with errors of 3.3 +- 2.9% and 4.3 +- 4.1% compared with the working space length, respectively. Such an adaptive controller also shows good performance on different control frequencies and desired velocities. This controller is also compared with a model-based controller in simulation. This controller endows soft robots with the potential for wide application, and future work may include different offline and online controllers. A weight parameter adjusting strategy may also be proposed in the future.

We study the problem of target stabilization with robust obstacle avoidance in robots and vehicles that have access only to vision-based sensors for the purpose of realtime localization. This problem is particularly challenging due to the topological obstructions induced by the obstacle, which preclude the existence of smooth feedback controllers able to achieve simultaneous stabilization and robust obstacle avoidance. To overcome this issue, we develop a vision-based hybrid controller that switches between two different feedback laws depending on the current position of the vehicle using a hysteresis mechanism and a data-assisted supervisor. The main innovation of the paper is the incorporation of suitable perception maps into the hybrid controller. These maps can be learned from data obtained from cameras in the vehicles and trained via convolutional neural networks (CNN). Under suitable assumptions on this perception map, we establish theoretical guarantees for the trajectories of the vehicle in terms of convergence and obstacle avoidance. Moreover, the proposed vision-based hybrid controller is numerically tested under different scenarios, including noisy data, sensors with failures, and cameras with occlusions.

Simulation to real (Sim-to-Real) is an attractive approach to construct controllers for robotic tasks that are easier to simulate than to analytically solve. Working Sim-to-Real solutions have been demonstrated for tasks with a clear single objective such as "reach the target". Real world applications, however, often consist of multiple simultaneous objectives such as "reach the target" but "avoid obstacles". A straightforward solution in the context of reinforcement learning (RL) is to combine multiple objectives into a multi-term reward function and train a single monolithic controller. Recently, a hybrid solution based on pre-trained single objective controllers and a switching rule between them was proposed. In this work, we compare these two approaches in the multi-objective setting of a robot manipulator to reach a target while avoiding an obstacle. Our findings show that the training of a hybrid controller is easier and obtains a better success-failure trade-off than a monolithic controller. The controllers trained in simulator were verified by a real set-up.

There have been attempts in model-based reinforcement learning to exploit a priori knowledge about the structure of the system. This paper introduces the extended radial basis function (RBF) controller design. In addition to traditional RBF controllers, our controller comprises of an engineered linear controller inside an operating region. We show that the learnt extended RBF controller takes on the desirable characteristics of both the linear and non-linear controller models. The extended controller is shown to retain the ability for universal function approximation of the non-linear RBF functions. At the same time, it demonstrates desirable stability criteria on par with the linear controller. Learning has been done in a probabilistic inference framework (PILCO), but could generalise to other reinforcement learning frameworks. Experimental results from the Swing-up pendulum, Cartpole, and Mountain car environments are reported.