Image to LiDAR Map Registration using Late Feature Projection
Zoom Link Abstract: Accurate localization is essential for autonomous operation in many problem domains. This is most often performed by comparing LiDAR scans collected in real-time to a HD point cloud based map. While this enables centimeter-level accuracy, it depends on an expensive LiDAR sensor at run time. Recently, efforts have been underway to reduce [...]
Carnegie Mellon University
Vision with Small Baselines
Zoom Link Abstract: 3D sensing with portable imaging systems is becoming more and more popular in computer vision applications such as autonomous driving, virtual reality, robotics manipulation and surveillance, due to the decreasing expense and size of RGB cameras. Despite the compactness and portability of the small baseline vision systems, it is well-known that the [...]
Carnegie Mellon University
Provably Constant-time Motion Planning
Zoom Link Abstract: In manufacturing and warehouse scenarios, robots often perform recurring manipulation tasks in structured environments. Fast and reliable motion planning is one of the key elements that ensure efficient operations in such environments. A very common example scenario is of manipulators working at conveyor belts, where they have limited time to pick moving [...]
Carnegie Mellon University
Humans In Their Natural Habitat: Training AI to Understand People
Zoom Link Abstract: Computer vision has a great potential to help our daily lives by searching for lost keys, watering flowers or reminding us to take a pill. To succeed with such tasks, computer vision methods need to be trained from real and diverse examples of our daily dynamic scenes. First, we need to give [...]
Task-specific Vision DNN Models and Their Relation for Explaining Different Areas of the Visual Cortex
Virtual VASC Seminar: https://cmu.zoom.us/j/249106600 Abstract: Deep Neural Networks (DNNs) are state-of-the-art models for many vision tasks. We propose an approach to assess the relationship between visual tasks and their task-specific models. Our method uses Representation Similarity Analysis (RSA), which is commonly used to find a correlation between neuronal responses from brain data and models. [...]
Carnegie Mellon University
A Theory of Fermat Paths for Non-line-of-sight Shape Reconstruction
Zoom Link Abstract: Traditionally, computer vision systems and algorithms, such as stereo vision, and shape from shading, have been developed to mimic human vision. As a consequence, a lot of these systems operate under constraints that we take for granted in human vision. An example of such a constraint is that the scene of interest [...]
Learning Contextual Actions for Heuristic Search-Based Motion Planning
Zoom Link Abstract: Heuristic search-based motion planning can be computationally costly in large state and action spaces. In this work we explore the use of generative models to learn contextual actions for successor generation in heuristic search. We focus on cases where the robot operates in similar environments, i.e. environments drawn from some underlying distribution. [...]
End-to-end Generative 3D Human Shape and Pose Models and Active Human Sensing
Virtual VASC Seminar: https://cmu.zoom.us/j/249106600 Title: End-to-end Generative 3D Human Shape and Pose Models and Active Human Sensing Abstract: I will review some of our recent work in 3d human modeling, synthesis, and active vision. I will present our new, end-to-end trainable nonlinear statistical 3d human shape and pose models of different resolutions (GHUM and GHUMLite) as [...]
Carnegie Mellon University
Safe and Resilient Multi-Robot Systems: Heterogeneity and Human Presence
Zoom Link Abstract: In the mission of a multi-robot team, the large number of robots behave like a system that relies on networking to enable smooth information propagation and inter-robot interaction as the mission evolves in a collective fashion. Key to the success of mission operation demands for safe and reliable robot interactions within the [...]
Carnegie Mellon University
Michael Tatum – MSR Thesis Talk
Archived Zoom Video Password: 1u%i4YO% Title: Communications Coverage in Unknown Underground Environments Abstract:In field robotics, maintaining communications between the user at a stationary basestation and all deployed robots is essential. This task's difficulty increases when the test environment is underground and the environment is unknown to the operator and robots. A common approach [...]
Carnegie Mellon University
Brendan Miller – MSR Thesis Talk
Zoom Link: https://cmu.zoom.us/j/96617143856 Title: IBB-Net: Fast Iterative Bounding Box Regression for Point Clouds Abstract: Currently, most point cloud based detection pipelines are focused on producing high accuracy results while requiring significant computational resources and a high-end GPU. Our research explores how to reduce the computational overhead by improving a key element of detection: bounding box regression. We [...]
Carnegie Mellon University
Interactive Weak Supervision – Learning Useful Heuristics for Data Labeling
Zoom Link Abstract: Obtaining large annotated datasets is critical for training successful machine learning models and it is frequently a bottleneck in practice. Weak supervision offers a promising alternative for producing labeled datasets without ground truth annotations by generating probabilistic labels using multiple noisy heuristics. This process can scale to large amounts of data and [...]
Carnegie Mellon University
Automated Action Selection and Embodied Simulation for Socially Assistive Robots using Standardized Interactions
Zoom Link Abstract: Robots have the tremendous potential of assisting people in their lives, allowing them to achieve goals that they would not be able to achieve by themselves. In particular, socially assistive robots provide assistance primarily through social interaction, in healthcare, therapy, and education contexts. Despite their potential, current socially assistive robots still lack [...]
Telling Left from Right: Learning Spatial Correspondence Between Sight and Sound
Virtual VASC Seminar: https://cmu.zoom.us/j/92741882813?pwd=R1R0eGRaeXFHTEF2VWNwY2VIZmU5Zz09 Abstract: Self-supervised audio-visual learning aims to capture useful representations of video by leveraging correspondences between visual and audio inputs. Existing approaches have focused primarily on matching semantic information between the sensory streams. In my talk, I’ll describe a novel self-supervised task to leverage an orthogonal principle: matching spatial information in the [...]
Carnegie Mellon University
Heuristics for routing and scheduling of Spatio-Temporal type problems in industrial environments
Zoom Link Abstract: Spatio-temporal problems are fairly common in industrial environments. In practice, these problems come with different characteristics and are often very hard to solve optimally. So practitioners prefer to develop heuristics that exploit mathematical structure specific to the problem for obtaining good performance. In this proposal, I will present work on heuristics for [...]
Carnegie Mellon University
Aditya Agarwal – MSR Thesis Talk
Zoom Link: https://cmu.zoom.us/j/3276236755 Title: Fast and High-Quality GPU-based Deliberative Perception for Object Pose Estimation Abstract: Pose estimation of known objects is fundamental to tasks such as robotic grasping and manipulation. The need for reliable grasping imposes stringent accuracy requirements on pose estimation in cluttered, occluded scenes in dynamic environments. Existing methods either require large sets of [...]
Carnegie Mellon University
Learning Active Task-Oriented Exploration Policies for Bridging the Sim-to-Real Gap
Zoom Link Abstract: Training robotic policies in simulation suffers from the sim-to-real gap, as simulated dynamics can be different from real-world dynamics. Past works tackled this problem through domain randomization and online system-identification. The former is sensitive to the manually-specified training distribution of dynamics parameters and can result in behaviors that are overly conservative. The [...]
Carnegie Mellon University
Understanding, Exploiting and Improving Inter-view Relationships
Zoom Link Abstract: Multi-view machine learning has received substantial attention in various applications over recent years. These applications typically involve learning on data obtained from multiple sources of information, such as, for example, in multi-sensor systems such as self-driving cars and patient bed-side monitoring. Learning models for such applications can often benefit from leveraging not [...]
Interferometric light transmission probing with coded mutual intensity
Zoom Link Abstract: We introduce a new interferometric imaging methodology that we term interferometry with coded mutual intensity, which allows selectively imaging photon paths based on attributes such as their length and endpoints. At the core of our methodology is a new technical result that shows that manipulating the spatial coherence properties of the light [...]
Carnegie Mellon University
Josh Jaekel – MSR Thesis Talk
Zoom Link: https://cmu.zoom.us/j/97161117200?pwd=QlpkS0hFOFVLRDlKVlFqby9JbWZTUT09 Title: Towards Robust Multi-Camera Visual Inertial Odometry Abstract: Visual inertial odometry (VIO) has become an increasingly popular method of obtaining a state estimate on board smaller robots like micro aerial vehicles (MAVs). While VIO has demonstrated impressive results in certain environments, there is still work to be done in improving the robustness of [...]
Carnegie Mellon University
John Mai – MSR Thesis Talk
Zoom Link: https://cmu.zoom.us/j/7518832261 Title: System Design, Modelling, and Control for an Off-Road Autonomous Ground Vehicle Abstract: Autonomy in passenger road vehicles has long been a goal for many research groups and companies, and there has been a significant amount of focus on achieving this endeavour. A lesser focused upon area is the task of precise autonomous [...]
Carnegie Mellon University
Zoom Link: https://cmu.zoom.us/j/5523238059 Title: Robust Instance Tracking via Uncertainty Flow Abstract: Current state-of-the-art trackers often fail due to distractors and large object appearance changes. In this work, we explore the use of dense optical flow to improve tracking robustness. Our main insight is that, because flow estimation can also have errors, we need [...]