Transparent household objects present a challenge for domestic service robots, since neither regular cameras nor RGB-D cameras can provide accurate points for shape reconstruction. The new type of pretouch dual-modality distance and material sensor (PDM2) can provide reliable and accurate depth readings, but it is a point sensor and scanning the object exclusively with the sensor is too inefficient. Hence, we present a sensor fusion approach by combining a regular camera with the PDM2sensor. The approach is based on a data fusion algorithm for shape reconstruction and an active perception algorithm for scan planning for the PDM2sensor. The data fusion algorithm is a distributed Gaussian process (GP)-based shape reconstruction method that allows for incremental local update to reduce computational time. The active perception algorithm is an optimization-based approach by increasing the information gain (IG) and prioritizing the boundary points under a preset travel distance constraint. We have implemented and tested the algorithms with six different transparent household items. The results show satisfactory shape reconstruction results in all test cases with an average increase in intersection over union (IoU) from 0.73 to 0.96.

Robotic weed removal in precision agriculture introduces a repetitive heterogeneous task planning (RHTP) challenge for a mobile manipulator. RHTP has two unique characteristics: 1) an observe-first-and-manipulate-later (OFML) temporal constraint that forces a unique ordering of two different tasks for each target and 2) energy savings from efficient task collocation to minimize unnecessary movements. RHTP can be framed as a stochastic renewal process. According to the Renewal Reward Theorem, the expected energy usage per task cycle is the long-run average. Traditional task and motion planning focuses on feasibility rather than optimality due to the unknown object and obstacle position prior to execution. However, the known target/obstacle distribution in precision agriculture allows minimizing the expected energy usage. For each instance in this renewal process, we first compute task space partition, a novel data structure that computes all possibilities of task multiplexing and its probabilities with robot reachability. Then we propose a region-based setcoverage problem to formulate the RHTP as a mixed-integer nonlinear programming. We have implemented and solved RHTP using Branch-and-Bound solver. Compared to a baseline in simulations based on real field data, the results suggest a significant improvement in path length, number of robot stops, overall energy usage, and number of replans.

Commercial plant phenotyping systems using fixed cameras cannot perceive many plant details due to leaf occlusion. In this paper, we present Botany-Bot, a system for building detailed “annotated digital twins” of living plants using two stereo cameras, a digital turntable inside a lightbox, an industrial robot arm, and 3D segmentated Gaussian Splat models. We also present robot algorithms for manipulating leaves to take high-resolution indexable images of occluded details such as stem buds and the underside/topside of leaves. Results from experiments suggest that Botany-Bot can segment leaves with 90.8% accuracy, detect leaves with 86.2% accuracy, lift/push leaves with 77.9% accuracy, and take detailed overside/underside images with 77.3% accuracy. Code, videos, and datasets are available at https://berkeleyautomation.github.io/Botany-Bot/.

Vision-Language Models (VLMs) acquire real-world knowledge and general reasoning ability through Internet-scale image-text corpora. They can augment robotic systems with scene understanding and task planning, and assist visuomotor policies that are trained on robot trajectory data. We explore the reverse paradigm — using rich, real, multi-modal robot trajectory data to enhance and evaluate VLMs. In this paper, we present Robo2VLM, a Visual Question Answering (VQA) dataset generation framework for VLMs. Given a human tele-operated robot demonstration with video and robot data, Robo2VLM derives ground-truth from non-visual and non-descriptive sensory modalities, such as end-effector pose, gripper aperture, and force sensing. Based on these modalities, it segments the robot trajectory into a sequence of manipulation phases. At each phase, Robo2VLM uses scene and interaction understanding to identify 3D properties of the robot, task goal, and the target object. The properties are used to generate representative VQA queries – images with textural multiple-choice questions – based on spatial, goal-conditioned, and interaction reasoning question templates. We use a subset of Open X-Embodiment to generate Robo2VLM-1, a large-scale in-the-wild dataset with 684,710 questions based on 463 distinct scenes and 3,396 robotic manipulation tasks from 176k real robot trajectories. Results suggest that Robo2VLM-1 can benchmark and improve VLM capabilities in spatial and interaction reasoning.

A mobile manipulator often finds itself in an application where it needs to take a close-up view before performing a manipulation task. Named this as a coupled active perception and manipulation (CAPM) problem, we model the uncertainty in the perception process and devise a key state/task planning algorithm that considers reachability conditions jointly established from perception and manipulation task constraints. By minimizing expected energy usage in body key state planning while satisfying task constraints, our algorithm is able to find an energy-efficient trajectory with less body repositioning motion while ensuring the success of the task. We have implemented the algorithm and tested it in both simulation and physical experiments. The results have confirmed that our algorithm has a lower energy consumption compared to a two-stage decoupled approach, while still maintaining a success rate of 100% for the task.

Robotic weed flaming is a new and environmentally friendly approach to weed removal in the agricultural field. Using a mobile manipulator equipped with a blowtorch, we design a new system and algorithm to enable effective weed flaming, which requires robotic manipulation with a soft and deformable end effector, as the thermal coverage of the flame is affected by dynamic or unknown environmental factors such as gravity, wind, atmospheric pressure, fuel tank pressure, and pose of the nozzle. System development includes overall design, hardware integration, and software pipeline. To enable precise weed removal, the greatest challenge is to detect and predict dynamic flame coverage in real time before motion planning, which is quite different from a conventional rigid gripper in grasping or a spray gun in painting. Based on the images from two onboard infrared cameras and the pose information of the blowtorch nozzle on a mobile manipulator, we propose a new dynamic flame coverage model. The flame model uses a center-arc curve with a Gaussian cross-section model to describe the flame coverage in real time. The experiments have demonstrated the working system and shown that our model and algorithm can achieve a mean average precision (mAP) of more than 76% in the reprojected images during online prediction.

We report a new material and structure mapping (MSM) algorithm to assist robotic grasping and manipulation. Building on our new sensor development, the algorithm has four main components: 1) detection of time-of-flight (ToF) durations for the dual modalities of optoacoustic (OA) and pulse-echo ultrasound (US), 2) contour reconstruction by fusing OA and US signals, 3) local noise filtering by checking local consistency of material and structure label (MSL), and 4) medium boundary searching that identifies class boundaries through two-staged clustering and boundary establishment using support vector machine (SVM) hyperplanes. We have implemented our algorithm and tested it with multiple common household items. The experimental results have successfully validated our algorithm design which shows that the average error of contour reconstruction is 0.05 mm and the true positive rate of MSL is over 98%.

Recently, Parallel Intrusion Detection (PID) becomes very popular and its procedure of the parallel processing is called a PID application (PIDA). This PIDA can be regarded as a Bag-of-Tasks (BoT) application, consisting of multiple tasks that can be processed in parallel. Given multiple PIDAs (i.e., BoT applications) to be handled, when the private cloud has insufficiently available resources to afford all tasks, some tasks have to be outsourced to public clouds with resource-used costs. The key challenge here is how to schedule tasks on hybrid clouds to minimize makespan given a limited budget. This problem can be formulated as an Integer Programming model, which is generally NP-Hard. Accordingly, in this paper, we construct an Iterated Local Search (ILS) algorithm, which employs an effective heuristic to obtain the initial task sequence and utilizes an insertion-neighbourhood-based local search method to explore better task sequences with lower makespans. A swap-based perturbation operator is adopted to avoid local optimum. With the objective of improving the proposal’s efficiency without loss of any effectiveness, to calculate task sequences’ objectives, we construct a Fast Task Assignment (FTA) method by integrating an existing Task Assignment (TA) method with an acceleration mechanism designed through theoretical analysis. Accordingly, the proposed ILS is named FILS. Experimental results show that FILS outperforms the existing best algorithm for the considered problem, considerably and significantly. More importantly, compared with TA, FTA achieves a 2.42x speedup, which verifies that the acceleration mechanism employed by FTA is able to remarkably improve the efficiency. Finally, impacts of key factors are also evaluated and analyzed, exhaustively.