Adept manipulation of articulated objects is essential for robots to operate successfully in human environments. Such manipulation requires both effectiveness—reliable operation despite uncertain object structures—and efficiency—swift execution with minimal redundant steps and smooth trajectories. Existing approaches struggle to achieve both objectives simultaneously: methods relying on predefined kinematic models lack robustness when encountering structural variations, while the tactile-informed approach achieves robust manipulation but sacrifices efficiency through reactive, step-by-step execute-and-recover cycles. To address this challenge, this paper introduces TacMan-Turbo, a proactive tactile control framework that unifies the cycles into a continuous control loop. Our key insight is to interpret tactile signals temporally in addition to spatially: instead of treating contact deviations merely as instantaneous error signals requiring immediate compensation, we analyze sequential tactile observations to reveal local kinematic information. This temporal perspective enables our controller to predict future object states and proactively modulate manipulation velocities, eliminating the previous execute-and-recover cycle. In evaluations across 200 diverse simulated articulated objects and real-world experiments, our approach maintains a near-perfect performance while significantly enhancing time efficiency, action efficiency, and trajectory smoothness (all p-values < 0.0001). These results demonstrate that tactile feedback serves a dual purpose: providing not only spatial error signals for reactive control but also critical temporal structural information that enables proactive prediction and control. By enabling robots to manipulate articulated objects both reliably and efficiently, this work advances robot capabilities for seamless operation in dynamic, human-centric environments.

Tactile sensing is crucial for achieving human-level robotic capabilities in manipulation tasks. As a promising solution, Vision-based Tactile Sensors (VBTSs) offer high spatial resolution and cost-effectiveness, but present unique challenges in robotics for their complex physical characteristics and visual signal processing requirements. The lack of efficient and accurate simulation tools for VBTSs has significantly limited the scale and scope of tactile robotics research. We present Taccel, a high-performance simulation platform that integrates Incremental Potential Contact (IPC) and Affine Body Dynamics (ABD) to model robots, tactile sensors, and objects with both accuracy and unprecedented speed, achieving a total of 915 FPS with 4096 parallel environments. Unlike previous simulators that operate at sub-real-time speeds with limited parallelization, Taccel provides precise physics simulation and realistic tactile signals while supporting flexible robot-sensor configurations through user-friendly APIs. Through extensive validation in object recognition, robotic grasping, and articulated object manipulation, we demonstrate precise simulation and successful sim-to-real transfer. These capabilities position Taccel as a powerful tool for scaling up tactile robotics research and development, potentially transforming how robots interact with and understand their physical environment.

Integrating robots into human-centric environments such as homes, necessitates advanced manipulation skills as robotic devices will need to engage with articulated objects such as doors and drawers. Key challenges in robotic manipulation of articulated objects are the unpredictability and diversity of these objects' internal structures, which render models based on object kinematics priors, both explicit and implicit, and inadequate. Their reliability is significantly diminished by pre-interaction ambiguities, imperfect structural parameters, encounters with unknown objects, and unforeseen disturbances. Here, we present aprior-freestrategy, Tac-Man, focusing on maintaining stable robot-object contact during manipulation. Without relying on object priors, Tac-Man leverages tactile feedback to enable robots to proficiently handle a variety of articulated objects, including those with complex joints, even when influenced by unexpected disturbances. Demonstrated in both real-world experiments and extensive simulations, it consistently achieves near-perfect success in dynamic and varied settings, outperforming existing methods. Our results indicate that tactile sensing alone suffices for managing diverse articulated objects, offering greater robustness and generalization than prior-based approaches. This underscores the importance of detailed contact modeling in complex manipulation tasks, especially with articulated objects. Advancements in tactile-informed approaches significantly expand the scope of robotic applications in human-centric environments, particularly where accurate models are difficult to obtain.

We present an optimization-based framework for rearranging indoor furniture to accommodate human-robot co-activities better. The rearrangement aims to afford sufficient accessible space for robot activities without compromising everyday human activities. To retain human activities, our algorithm preserves the functional relations among furniture by integrating spatial and semantic co-occurrence extracted from SUNCG and ConceptNet, respectively. By defining the robot's accessible space by the amount of open space it can traverse and the number of objects it can reach, we formulate the rearrangement for human-robot co-activity as an optimization problem, solved by adaptive simulated annealing (ASA) and covariance matrix adaptation evolution strategy (CMA-ES). Our experiments on the SUNCG dataset quantitatively show that rearranged scenes provide a robot with 14% more accessible space and 30% more objects to interact with on average. The quality of the rearranged scenes is qualitatively validated by a human study, indicating the efficacy of the proposed strategy.

Modern herbicide application in agricultural set-tings typically relies on either large scale sprayers that dispense herbicide over crops and weeds alike or portable sprayers that require labor intensive manual operation. The former method results in overuse of herbicide and reduction in crop yield while the latter is often untenable in large scale operations. This paper presents the first fully autonomous robot for weed management for row crops capable of computer vision based navigation, weed detection, complete field coverage, and automatic recharge for under $400. The target application is autonomous inter-row weed control in crop fields, e.g. flax and canola, where the spacing between croplines is as small as one foot. The proposed robot is small enough to pass between croplines at all stages of plant growth while detecting weeds and spraying herbicide. A recharging system incorporates newly designed robotic hardware, a ramp, a robotic charging arm, and a mobile charging station. An integrated vision algorithm is employed to assist with charger alignment effectively. Combined, they enable the robot to work continuously in the field without access to electricity. In addition, a color-based contour algorithm combined with preprocessing techniques is applied for robust navigation relying on the input from the onboard monocular camera. Incorporating such compact robots into farms could help automate weed control, even during late stages of growth, and reduce herbicide use by targeting weeds with precision. The robotic platform is field-tested in the flaxseed fields of North Dakota.

This paper is focused on investigations of the improved correction of the effect of variation in incidence angle on ScanSAR data. Conventional correction methods (such as LLM, locally linear mapping) typically assume that each target class has a similarity distribution in the middle of the image. The objectives of this study are to extend the correction algorithm to full swath width without any assumptions. For a target class only distributed on one or both sides of the image, interpolation or extrapolation of the confidence interval is realized using the linear regression technique based on the exponential model. The position of the reference band is then determined and the correction is performed. Experiments were performed on ENVISAT ASAR and RADARSAT-2 ScanSAR data. The results show the effectiveness of the proposed method.