Plastics recycling is a critical ecological and economic solution to manage plastic waste, yet a staggering proportion of plastics from daily use is landfilled or incinerated. A critical step to recycling plastics is our ability to sort plastics by type (HDPE, LDPE, PET, PP, PS, and PVC) at a mixed recycling facility. However, challenges such as sensor system cost, difficulty in data collection, and the dense sampling required for model fine-tuning continue to hinder reliable large-scale deployment and limit progress toward a sustainable circular plastic economy. In this work, we propose a novel physics-informed plastics classification system based on active thermal imaging. Additionally, we present a two-stage training strategy that uses a large quantity of easily generated PDE-based simulated samples for pretraining and fine-tunes the model to real-world data distributions using only a sparse set of samples. We validate the efficacy of the proposed approach on real-world plastic samples. Thus, we introduce Thermal Imaging based Plastic Sorting (TIPS), a system that achieves up to 100% and 94.7% accuracy in plastic type classification for black and white plastics, respectively.

Volumetric learning underpins many 3D vision tasks such as completion, reconstruction, and mesh generation, yet training objectives still rely on Chamfer Distance (CD) or Earth Mover’s Distance (EMD), which fail to balance recall and precision. We propose Quality-Aware Loss (QAL), a drop-in replacement for CD/EMD that combines a coverage-weighted nearest-neighbor term with an uncovered-ground-truth attraction term, explicitly decoupling recall and precision into tunable components. Across diverse pipelines, QAL achieves consistent coverage gains, improving by an average of +4.3 pts over CD and +2.8 pts over the best alternatives. These improvements reliably recover thin structures and under-represented regions that CD/EMD overlook. Extensive ablations confirm stable performance across hyperparameters and output resolutions, while full retraining on PCN and ShapeNet demonstrates generalization across datasets and backbones. Moreover, QAL-trained completions yield higher grasp scores under GraspNet evaluation, showing that improved coverage translates directly into more reliable robotic manipulation. QAL thus offers a principled, interpretable, and practical objective for robust 3D vision and safety-critical robotics pipelines.

Plastics detection using mobile apps can greatly assist in plastics sorting at the source and allow great improvements in the percentage of plastics that are recycled. Previous work such as DeepWaste and MWaste has tackled general waste classification, including plastics, but few efforts focus on real-time plastic type identification on mobile devices. CLIPS addresses this gap by developing a mobile app in combination with a cloud service that enables plastic-material classification. Further, CLIPS utilizes a continual learning architecture to adapt the model to the local stream of plastics, allowing for greater detection accuracy over time. We demonstrate that our approach can improve performance by 37% through continual learning compared to a pre-trained model. We also demonstrate a positive backward transfer of +27% and a forward transfer of +19.63% with continual learning over time.

Historically, feature-based approaches have been used extensively for camera-based robot perception tasks such as localization, mapping, tracking, and others. Several of these approaches also combine other sensors (inertial sensing, for example) to perform combined state estimation. Our work rethinks this approach; we present a representation learning mechanism that identifies visual features that best correspond to robot motion as estimated by an external signal. Specifically, we utilize the robot’s transformations through an external signal (inertial sensing, for example) and give attention to image space that is most consistent with the external signal. We use a pairwise consistency metric as a representation to keep the visual features consistent through a sequence with the robot’s relative pose transformations. This approach enables us to incorporate information from the robot’s perspective instead of solely relying on the image attributes. We evaluate our approach on real-world datasets such as KITTI & EuRoC and compare the refined features with existing feature descriptors. We also evaluate our method using our real robot experiment. We notice an average of 49% reduction in the image search space without compromising the trajectory estimation accuracy. Our method reduces the execution time of visual odometry by 4.3% and also reduces reprojection errors. We demonstrate the need to select only the most important features and show the competitiveness using various feature detection baselines.

This paper presents an innovative approach to archaeological and geological exploration, combining Synthetic Aperture Radar (SAR) imagery, Ground Penetrating Radar (GPR), and advanced robotic algorithms. Utilizing SAR data captured by Capella, the study identifies areas of interest (AOIs) through supervised classification methods. These AOIs are then surveyed by a UAV equipped with GPR, optimized for efficient pathfinding and maximal coverage using robotic exploration algorithms. The survey generates high-resolution radar images, detailed digital elevation models, and orthomosaic images through photogrammetry, providing a comprehensive view of both surface and subsurface features.

Mapping 3D airflow fields is important for many HVAC, industrial, medical, and home applications. However, current approaches are expensive and time-consuming. We present Anemoi, a sub-$100 drone-based system for autonomously mapping 3D airflow fields in indoor environments. Anemoi leverages the effects of airflow on motor control signals to estimate the magnitude and direction of wind at any given point in space. We introduce an exploration algorithm for selecting optimal waypoints that minimize overall airflow estimation uncertainty. We demonstrate through microbenchmarks and real deployments that Anemoi is able to estimate wind speed and direction with errors up to 0.41 m/s and 25.1° lower than the existing state of the art and map 3D airflow fields with an average RMS error of 0.73 m/s.

Spatial sensing is a fundamental requirement for applications in robotics and augmented reality. In urban spaces such as malls, airports, apartments, and others, it is quite challenging for a single robot to map the whole environment. So, we employ a swarm of robots to perform the mapping. One challenge with this approach is merging sub-maps built by each robot. In this work, we use wireless access points, which are ubiquitous in most urban spaces, to provide us with coarse orientation between sub-maps, and use a custom ICP algorithm to refine this orientation to merge them. We demonstrate our approach with maps from a building on campus and evaluate it using two metrics. Our results show that, in the building we studied, we can achieve an average Absolute Trajectory error of 0.2m in comparison to a map created by a single robot and average Root Mean Square mapping error of 1.3m from ground truth landmark locations.

Simultaneous Localization and Mapping (SLAM) is the process of learning about both the environment and about a robot's location with respect to the environment and is essential for robots to autonomously navigate. A variety of algorithms using many different sensors such as RGB-D cameras, laser range finders, ultrasonic sensors and others have been proposed to perform SLAM. However, these algorithms face common challenges are that of computational complexity, wrong loop closure detection and failure to localize correctly when robot loses state (kidnapped robot problem). In this work, we utilize Wi-Fi signal strength sensing to aid the SLAM process in indoor environments and address the challenges mentioned above.