Simulation-based Planning for Pick-and-Place in Heavy Clutter using Non-prehensile Manipulation
Abstract: Robot manipulation in domestic households, industrial manufacturing and warehouses might require contact-rich interactions with objects in the environment. For pick-and-place style grasping tasks in cluttered scenes, it can be more economical for the robot to rely on non-prehensile actions vis-à-vis deliberate prehensile rearrangement. Non-prehensile actions also let the robot manipulate large and bulky objects [...]
Carnegie Mellon University
Relationships in instance segmentation and anomaly detection
Abstract: This thesis primarily covers work on two different tasks in computer vision: (1) anomaly detection and (2) instance segmentation. Anomaly detection is an underexplored unsupervised problem that has existed in many fields. On the other hand, instance (and panoptic) segmentation is a supervised problem that can leverage the powerful data and key developments from [...]
Next-Gen Video Communication
Abstract: Video communication connects our world. It is necessary in conducting business, educational and personal activities across different geographical locations. However, the quality of an average user’s video communication is dramatically worse than that of professionally created videos in news broadcasts, talk shows, and on YouTube. This is because professionally created videos are often captured with [...]
Carnegie Mellon University
Learning with Diverse Forms of Imperfect and Indirect Supervision
Abstract: High capacity Machine Learning (ML) models trained on large, annotated datasets have driven impressive advances in several fields including natural language processing and computer vision, in turn leading to impactful applications of ML in areas such as healthcare, e-commerce, and predictive maintenance. However, obtaining annotated datasets at the scale required for training such models [...]
MRSD Annual Poster Presentation
Four student teams from the MRSD program will use posters, videos, and hardware to show their project work on robots for room disinfection, search & rescue, increasing human capability via a third arm, and increased-efficiency factory-floor obstacle avoidance.
Carnegie Mellon University
3D Representation Learning for Perception and Prediction: A Modular Yet Highly Integrated Approach
Abstract: Modularized and cascaded autonomy stacks (object detection, then tracking and then trajectory prediction) have been widely adopted in many autonomous systems such as self-driving cars due to its interpretability. In this talk, I advocate the use of such a modular approach but improve its accuracy and robustness by developing different 3D representations for each [...]
Carnegie Mellon University
MSR Thesis Talk: Avi Rudich
Title: Kinematic Analysis of 3D Printed Flexible Delta Robots Abstract: Flexible Delta robots show significant promise for use in a wide array of manipulation tasks. They are simple to design and manufacture, and they maintain a high level of repeatability and precision in open loop control. This thesis analyzes the kinematic properties of flexible [...]
Reconstructing common objects to interact with
Abstract: We humans are able to understand 3D shapes of common daily objects and interact with them from a wide range of categories. We understand cups are usually cylinder-like and we can easily predict the shape of one particular cup, both in isolation or even when it is held by a human. We aim to [...]
Activity Understanding of Scripted Performances
Abstract: The PSU Taichi for Smart Health project has been doing a deep-dive into vision-based analysis of 24-form Yang-style Taichi (TaijiQuan). A key property of Taichi, shared by martial arts katas and prearranged form exercises in other sports, is practice of a scripted routine to build both mental and physical competence. The scripted nature of routines [...]
Carnegie Mellon University
Dynamical Model Learning and Inversion for Aggressive Quadrotor Flight
Abstract: Quadrotor applications have seen a surge recently and many tasks require precise and accurate controls. Flying fast is critical in many applications and the limited onboard power source makes completing tasks quickly even more important. Staying on a desired course while traveling at high speeds and high accelerations is difficult due to complex and [...]
Carnegie Mellon University
Person Transfers Between Multiple Service Robots
Abstract: As more service robots are deployed in the world, human-robot interaction will not be limited to one-to-one interactions between users and robots. Instead, users will likely have to interact with multiple robots, simultaneously or sequentially, throughout their day to receive services and complete different tasks. In this thesis, I describe work in which my [...]
A causal framework to diagnose and fix issues with doors
Abstract: Many animals, such as ravens, (and a fortiori humans) exhibit a great deal of physical intelligence that allows them to solve complex multi-step physical puzzles. This ability indicates an understanding or a faculty to represent causality and mechanisms, understand when something goes wrong, and figure out how to deal with it. As a step [...]
Carnegie Mellon University
Understanding Unbalanced Datasets Through Simple Models and Dataset Exploration
Abstract: Computer vision models have proven to be tremendously capable of recognizing and detecting several classes and objects. They succeed in classes widely ranging in type and scale from humans to cans to pens. However, the best performing classes have abundant examples in large-scale datasets today. In unbalanced datasets, where some categories are seen in [...]
Domain adaptive object detection
Abstract: Recent advances in deep learning have led to the development of accurate and efficient models for object detection. However, learning highly accurate models relies on the availability of large-scale annotated datasets. Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images. Domain adaptation tries to mitigate this degradation. In [...]
Carnegie Mellon University
Understanding, Exploiting and Improving Inter-view Relationships
Abstract: Multi-view machine learning has garnered substantial attention in various applications over recent years. Many such applications involve learning on data obtained from multiple heterogeneous sources of information, for example, in multi-sensor systems such as self-driving cars, or monitoring intensive care patient vital signs at their bed-side. Learning models for such applications can often benefit [...]
Model-Centric Verification of Artificial Intelligence
Abstract: This work shows how provable guarantees can be used to supplement probabilistic estimates in the context of Artificial Intelligence (AI) systems. Statistical techniques measure the expected performance of a model, but low error rates say nothing about the ways in which errors manifest. Formal verification of model adherence to design specifications can yield certificates [...]
Designing Whisker Sensors to Detect Multiple Mechanical Stimuli for Robotic Applications
Abstract: Many mammals, such as rats and seals, use their whiskers as versatile mechanical sensors to gain precise information about their surroundings. Whisker-inspired sensors on robotic platforms have shown their potential benefit, improving applications ranging from drone navigation to texture mapping. Despite this, there is a gap between the engineered sensors and many of the [...]
Carnegie Mellon University
Human-in-the-loop Control of Mobile Robots
Abstract: Human-in-the-loop control for mobile robots is an important aspect of robot operation, especially for navigation in unstructured environments or in the case of unexpected events. However, traditional paradigms of human-in-the-loop control have relied heavily on the human to provide precise and accurate control inputs to the robot, or reduced the role of the human [...]
Visual Understanding across Semantic Groups, Domains and Devices
Abstract: Deep neural networks often lack generalization capabilities to accommodate changes in the input/output domain distributions and, therefore, are inherently limited by the restricted visual and semantic information contained in the original training set. In this talk, we argue the importance of the versatility of deep neural architectures and we explore it from various perspectives. [...]
Towards Robust Human-Robot Interaction: A Quality Diversity Approach
Abstract: The growth of scale and complexity of interactions between humans and robots highlights the need for new computational methods to automatically evaluate novel algorithms and applications. Exploring the diverse scenarios of interaction between humans and robots in simulation can improve understanding of complex human-robot interaction systems and avoid potentially costly failures in real-world settings. [...]
Carnegie Mellon University
Planning and Execution using Inaccurate Models with Provable Guarantees on Task Completeness
Abstract: Modern planning methods are effective in computing feasible and optimal plans for robotic tasks when given access to accurate dynamical models. However, robots operating in the real world often face situations that cannot be modeled perfectly before execution. Thus, we only have access to simplified but potentially inaccurate models. This imperfect modeling can lead [...]
Topology-Driven Learning for Biomedical Imaging Informatics
Abstract: Thanks to decades of technology development, we are now able to visualize in high quality complex biomedical structures such as neurons, vessels, trabeculae and breast tissues. We need innovative methods to fully exploit these structures, which encode important information about underlying biological mechanisms. In this talk, we explain how topology, i.e., connected components, handles, loops, [...]
Lessons from the Field: Deep Learning and Machine Perception for field robots
Abstract: Mobile robots now deliver vast amounts of sensor data from large unstructured environments. In attempting to process and interpret this data there are many unique challenges in bridging the gap between prerecorded data sets and the field. This talk will present recent work addressing the application of machine learning techniques to mobile robotic perception. [...]
Learning generative representations for image distributions
Abstract: Autoencoder neural networks are an unsupervised technique for learning representations, which have been used effectively in many data domains. While capable of generating data, autoencoders have been inferior to other models like Generative Adversarial Networks (GAN’s) in their ability to generate image data. We will describe a general autoencoder architecture that addresses this limitation, and [...]
Carnegie Mellon University
Self-Supervising Occlusions for Vision
Abstract: Virtually every scene has occlusions. Even a scene with a single object exhibits self-occlusions - a camera can only view one side of an object (left or right, front or back), or part of the object is outside the field of view. More complex occlusions occur when one or more objects block part(s) of [...]
Carnegie Mellon University
Development of an Agile and Dexterous Balancing Mobile Manipulator Robot
Abstract: This thesis focuses on designing and controlling a dynamically stable shape-accelerating dual-arm mobile manipulator, the Carnegie Mellon University (CMU) ballbot. The CMU ballbot is a human-sized dynamically stable mobile robot that balances on a single spherical wheel. We describe the development of a pair of seven-degree-of-freedom (DOF) humanoid arms. The new 7-DOF arm pair [...]
Carnegie Mellon University
Massively Parallelized Lazy Planning Algorithms
Abstract: Search-based planning algorithms enable autonomous agents like robots to come up with well-reasoned long horizon plans to achieve a given task objective. They do so by optimizing a task-specific cost function while respecting the constraints on either the agent (e.g. motion constraints) or the environment (e.g. obstacles). In robotics, such as in motion planning [...]
Building Intelligent and Visceral Machines: From Sensing to Application
Abstract: Humans have evolved to have highly adaptive behaviors that help us survive and thrive. As AI prompts a move from computing interfaces that are explicit and procedural to those that are implicit and intelligent, we are presented with extraordinary opportunities. In this talk, I will argue that understanding affective and behavioral signals presents many opportunities [...]
GANcraft – an unsupervised 3D neural method for world-to-world translation
Abstract: Advances in 2D image-to-image translation methods, such as SPADE/GauGAN, have enabled users to paint photorealistic images by drawing simple sketches similar to those created in Microsoft Paint. Despite these innovations, creating a realistic 3D scene remains a painstaking task, out of the reach of most people. It requires years of expertise, professional software, a library [...]
Run-Time Optimization in the Deep Learning Age
Abstract: In a recovery task one seeks to obtain an estimate of an unknown signal from a set of incomplete measurements. These problems arise in a number of computer vision applications, from image based tasks such as super-resolution and in-painting to 3D reconstruction tasks such as Non-Rigid Structure from Motion and scene flow estimation. Early [...]
Carnegie Mellon University
System Identification and Control of Multiagent Systems Through Interactions
Abstract: This thesis investigates the problem of identifying dynamics models of individual agents of a multiagent system (MAS) and exploiting these models to shape their behavior using robots extrinsic to the MAS. While task-based control of a MAS using onboard controllers of its agents is well studied, we investigate (a) how easy it is for [...]
Human-in-the-loop Model Creation
Abstract: Modern machine learning systems have made astonishing progress in automating labor-intensive tasks such as visual recognition and machine translation. While ML systems complete these tasks better and faster, humans are largely left behind. Indeed, most humans are entirely excluded from the creation process of machine learning models, except for tedious data annotation. In [...]
Carnegie Mellon University
Learning and Inference in Factor Graphs with Applications to Tactile Perception
Abstract: Factor graphs offer a flexible and powerful framework for solving large-scale, nonlinear inference problems as encountered in robot perception and control. Typically, these methods rely on handcrafted models that are efficient to optimize. However, robots often perceive the world through complex, high-dimensional sensor observations. For instance, consider a robot manipulating an object in hand [...]
Learning Optical Flow: Model, Data, and Applications
Abstract: Optical flow provides important information about the dynamic world and is of fundamental importance to many tasks. In this talk, I will present my work on different aspects of learning optical flow. I will start with the background and talk about PWC-Net, a compact and effective model built using classical principles for optical flow. Next, [...]
Hands-On Interactions
Abstract: Our sense of touch is present in almost all our interactions with the world, from providing us with the feedback necessary to perceive and manipulate objects without having to look at them, to allowing our limbs to move and walk without us having to think about how to take the next step. We use [...]
Distributed Dissipativity: Applying Foundational Stability Theory to Modern Networked Control
Abstract: Despite its diverse areas of application, the desire to optimize performance and guarantee acceptable behaviour in the face of inevitable uncertainty is pervasive throughout control theory. This creates a fundamental challenge since the necessity of robustly stable control schemes often favors conservative designs, while the desire to optimize performance typically demands the opposite. While [...]
Towards Complex Robot Motions with Reinforcement Learning
Abstract: Reinforcement learning has shown to be a powerful tool for decision-making problems. In this talk, we present the opportunities and challenges of enabling increasingly complex robot behavior with reinforcement learning. First, we present a system that combines reinforcement learning and extrinsic dexterity to solve a novel task of “occluded grasping”. To reach an occluded [...]
Haptic Perspective-taking from Vision and Force
Abstract: Physically collaborative robots present an opportunity to positively impact society across many domains. However, robots currently lack the ability to infer how their actions physically affect people. This is especially true for robotic caregiving tasks that involve manipulating deformable cloth around the human body, such as dressing and bathing assistance. In this talk, I [...]
Do Vision-Language Pretrained Models Learn Spatiotemporal Primitive Concepts?
Abstract: Vision-language models pretrained on web-scale data have revolutionized deep learning in the last few years. They have demonstrated strong transfer learning performance on a wide range of tasks, even under the "zero-shot" setup, where text "prompts" serve as a natural interface for humans to specify a task, as opposed to collecting labeled data. These models are [...]
RI Council Meeting
RI Council is a leadership group made up of the Director of RI, Academic Program Leads, Committee Chairs, and members at large as appointed by the Director. RI Council meets generally once a week to discuss department business.
Perception-Action Synergy in Uncertain Environments
Abstract: Many robotic applications require a robot to operate in an environment with unknowns or uncertainty, at least initially, before it gathers enough information about the environment. In such a case, a robot must rely on sensing and perception to feel its way around. Moreover, it has to couple sensing/perception and motion synergistically in real [...]
Carnegie Mellon University
Driving Reconfigurable Unmanned Vehicle Design for Mobility Performance
Abstract: Unmanned ground vehicles are being deployed in increasingly diverse and complex environments. Advances in the field of robotics, including perception technology, computing power, and machine learning, have brought robots from the lab to the real world. Remote and autonomous vehicles are now used to explore volcanoes, caves, pipes, war zones, disaster sites, and even [...]
Max-Affine Spline Insights into Deep Learning
Abstract: We build a rigorous bridge between deep networks (DNs) and approximation theory via spline functions and operators. Our key result is that a large class of DNs can be written as a composition of max-affine spline operators (MASOs) that provide a powerful portal through which we view and analyze their inner workings. For instance, [...]
Search-based Path Planning for a High Dimensional Manipulator in Cluttered Environments Using Optimization-based Primitives
Abstract: In this work we tackle the path planning problem for a 21-dimensional snake robot-like, navigating a cluttered gas turbine for the purposes of inspection. Heuristic search-based approaches are effective planning strategies for common manipulation domains. However, their performance on high-dimensional systems is heavily reliant on the effectiveness of the action space and the heuristics [...]
Vision-Based Tactile Sensor Design using Physics Based Rendering
Abstract: Tactile sensing has seen a rapid adoption with the advent of vision-based tactile sensors. Vision-based tactile sensors provide high resolution, compact and inexpensive data to perform precise in-hand manipulation and human-robot interaction. However, the simulation of tactile sensors is still a challenge. Simulation is a critical tool in the development of robotic systems. In [...]
Teruko Yata Memorial Lecture
Leveraging Language and Video Demonstrations for Learning Robot Manipulation Skills and Enabling Closed-Loop Task Planning Humans have gradually developed language, mastered complex motor skills, created and utilized sophisticated tools. The act of conceptualization is fundamental to these abilities because it allows humans to mentally represent, summarize and abstract diverse knowledge and skills. By means of [...]
Details to Follow . . .
Details to Follow . . .
Robotics Institute Staff Offices 12PM Early Dismissal
Dear RI Faculty and Staff, In observance of the coming holiday, institute staff offices will close Friday, April 15th at noon. They will reopen at 8:30 on Monday, April 18th. Happy Holiday! Thank you – Debbie Z. =================================================== Deborah H. Zalewski, Senior Associate Business Manager | The Robotics Institute - Carnegie Mellon University | Newell-Simon [...]
RI Hiring Meeting
A faculty hiring meeting to discuss candidates for faculty position
Carnegie Mellon University
Unified Simulation, Perception, and Generation of Human Behavior
Abstract: Understanding and modeling human behavior is fundamental to almost any computer vision and robotics applications that involve humans. In this thesis, we take a holistic approach to human behavior modeling and tackle its three essential aspects --- simulation, perception, and generation. Throughout the thesis, we show how the three aspects are deeply connected and [...]
Kernel Density Decision Trees
Abstract We propose kernel density decision trees (KDDTs), a novel fuzzy decision tree (FDT) formalism based on kernel density estimation that improves the robustness of decision trees and ensembles and offers additional utility. FDTs mitigate the sensitivity of decision trees to uncertainty by representing uncertainty through fuzzy partitions. However, compared to conventional, crisp decision trees, [...]
Energy-based Joint Pose Estimation for 3D Reconstruction
Abstract: In this talk, I will describe a data-driven method for inferring camera poses given a sparse collection of images of an arbitrary object. This task is a core component of classic geometric pipelines such as structure-from-motion (SFM), and also serves as a vital pre-processing requirement for contemporary neural approaches (e.g. NeRF) to object reconstruction. [...]
NeRF for Robotics
Abstract: In this talk I'll describe how recent advances in neural rendering and novel view synthesis - namely NeRF - can be leveraged by robotic agents to improve performance in manipulation tasks. Specifically, I'll argue that NeRF can enable robotic policies to: (1) generalize to new viewpoints; (2) perceive specular and reflective surfaces in a [...]
Carnegie Mellon University
Search Algorithms and Search Spaces for Neural Architecture Search
Abstract: Neural architecture search (NAS) is recently proposed to automate the process of designing network architectures. Instead of manually designing network architectures, NAS automatically finds the optimal architecture in a data-driven way. Despite its impressive progress, NAS is still far from being widely adopted as a common paradigm for architecture design in practice. This thesis [...]
Carnegie Mellon University
MSR Thesis Talk – Evan Harber
Title: Stiffness Mapping of Deformable Objects Through Supervised Embedding and Gaussian Process Regression Abstract: The stiffness map of a deformable object stores information about that object's surface compliance. Thus, through a stiffness map, we gain insight into the physical properties of that object. Depending on the object, an understanding of stiffness has applications ranging [...]
Designing Robotic Systems with Collective Embodied Intelligence
Abstract: Natural swarms exhibit sophisticated colony-level behaviors with remarkable scalability and error tolerance. Their evolutionary success stems from more than just intelligent individuals, it hinges on their morphology, their physical interactions, and the way they shape and leverage their environment. Mound-building termites, for instance, are believed to use their own body as a template for [...]
MSR Thesis Talk – Gaurav Parmar
Title: Spatially-Adaptive Multilayer GAN Inversion Abstract: Existing GAN inversion and editing methods are well suited for only a target images that contain aligned objects with a clean background, such as portraits and animal faces, but often struggle for more difficult categories with complex scene layouts and object occlusions, such as cars, animals, and outdoor images. [...]
Robust Reinforcement Learning via Genetic Curriculum
Abstract: Achieving robust performance is crucial when applying deep reinforcement learning (RL) in safety critical systems. Some of the state of the art approaches try to address the problem with adversarial agents, but these agents often require expert supervision to fine tune and prevent the adversary from becoming too challenging to the trainee agent. While [...]
Mouth Haptics in VR using a Headset Ultrasound Phased Array
Abstract: This talk is the same one I will be presenting at the ACM CHI Conference on Human Factors in Computing Systems on May 2nd. Paper abstract: Today’s consumer virtual reality (VR) systems offer limited haptic feedback via vibration motors in handheld controllers. Rendering haptics to other parts of the body is an open challenge, [...]
Towards Large-scale and Long-term Neural Map Representations
Abstract: We address the problem of large-scale and long-term neural map representations. Maps, as our prior understanding toward the environment, provide valuable information for modern robotics applications such as autonomous driving and AR/VR. The size of maps largely affects the end task performance: usually a more detailed map can support better performance, but would cost [...]
Carnegie Mellon University
Self-Improving 3D Scene Representations
Abstract: Most computer vision models in deployment today are not continually learning. Instead, they are in a “test” mode, where they will behave the same way perpetually, until they are replaced by newer models. This is a problem, because it means the models may perform poorly as soon as their “test” environment diverges from their [...]
Carnegie Mellon University
MSR Thesis Talk – Manash Pratim Das
Title: Model-Accuracy Aware Anytime Planning with Simulation Verification for Navigating Complex Terrains Abstract: Off-road and unstructured environments often contain complex patches of various types of terrain, rough elevation changes, deformable objects, etc. An autonomous ground vehicle traversing such environments experiences physical interactions that are extremely hard to model at scale and thus very hard to [...]
Carnegie Mellon University
MSR Thesis Talk – Akshay Dharamavaram
Title: Stabilizing the Training Dynamics of Generative Models using Self-Supervision Abstract: Generative Models have been shown to be adept in mimicking the behavior of an unknown distribution solely from bootstrapped data. However, deep learning models have been shown to overfit in either the minimization or maximization stage of the two player min-max game, resulting [...]
Carnegie Mellon University
Direct-drive Hands: Making Robot Hands Transparent and Reactive to Contacts
Abstract: Industrial manipulators and end-effectors are a vital driver of the automation revolution. These robot hands, designed to reject disturbances with stiffness and strength, are inferior to their human counterparts. Human hands are dexterous and nimble effectors capable of a variety of interactions with the environment. Through this thesis we wish to answer a question: [...]
Carnegie Mellon University
MSR Thesis Talk – Vivek Roy
Title: Smartphone localization for Indoor Pedestrian Navigation Abstract: Global positioning system (GPS) interfacing with applications such as Google Maps has proven very useful for navigation in outdoor open settings. However in crowded metropolitan environments with high rise buildings or in indoor settings, GPS quickly becomes unreliable. Using sensors found on commodity smartphones to perform accurate [...]
Understanding 3D Scenes and Interacting Hands
Abstract: Abstract: The long-term goal of my research is to help computers understand the physical world from images, including both 3D properties and how humans or robots could interact with things. This talk will summarize two recent directions aimed at enabling this goal. I will begin with learning to reconstruct full 3D scenes, including [...]
Manipulating Objects with Challenging Visual and Geometric Properties
Abstract: Object manipulation is a well-studied domain in robotics, yet manipulation remains difficult for objects with visually and geometrically challenging properties. Visually challenging properties, such as transparency and specularity, break assumptions of Lambertian reflectance that existing methods rely on for grasp estimation. On the other hand, deformable objects such as cloth pose both visual and [...]
TIGRIS: An Informed Sampling-based Algorithm for Informative Path Planning
Abstract: In this talk I will present our sampling-based approach to informative path planning that allows us to tackle the challenges of large and high-dimensional search spaces. This is done by performing informed sampling in the high-dimensional continuous space and incorporating potential information gain along edges in the reward estimation. This method rapidly generates a [...]
Carnegie Mellon University
MSR Thesis Talk – Zhe Huang
Title: Distributed Reinforcement Learning for Autonomous Driving Abstract: Due to the complex and safety-critical nature of autonomous driving, recent works typically test their ideas on simulators designed for the very purpose of advancing self-driving research. Despite the convenience of modeling autonomous driving as a trajectory optimization problem, few of these methods resort to online reinforcement [...]
Carnegie Mellon University
MSR Thesis Talk- Xinjie Yao
Title: Ride Comfort-Aware Visual Navigation via Self-Supervised Learning Abstract: Under shared autonomy, wheelchair users expect vehicles to provide safe and comfortable rides while following users’ high-level navigation plans. To find such a path, vehicles negotiate with different terrains and assess their traversal difficulty. Most prior works model surroundings either through geometric representations or semantic classifications, [...]
MS Thesis Talk – Shun Iwase
Title: Fast 6D Object Pose Refinement via Deep Texture Rendering Abstract: We present RePOSE, a fast iterative refinement method for 6D object pose estimation. Prior methods perform refinement by feeding zoomed-in input and rendered RGB images into a CNN and directly regressing an update of a refined pose. Their runtime is slow due to the [...]
Carnegie Mellon University
Resource-Constrained Learning and Inference for Visual Perception
Abstract: We have witnessed rapid advancement across major computer vision benchmarks over the past years. However, the top solutions' hidden computation cost prevents them from being practically deployable. For example, training large models until convergence may be prohibitively expensive in practice, and autonomous driving or augmented reality may require a reaction time that rivals that [...]
Trajectory Optimization for Thermally-Actuated Soft Planar Robot Limbs
Abstract: Practical use of robotic manipulators made from soft materials requires generating and executing complex motions. We present the first approach for generating trajectories of a thermally-actuated soft robotic manipulator. Based on simplified approximations of the soft arm and its antagonistic shape-memory alloy actuator coils, we justify a dynamics model of a discretized rigid manipulator [...]
Carnegie Mellon University
Physical Interaction and Manipulation of the Environment using Aerial Robots
Abstract: The physical interaction of aerial robots with their environment has countless potential applications and is an emerging area with many open challenges. Fully-actuated multirotors have been introduced to tackle some of these challenges. They provide complete control over position and orientation and eliminate the need for attaching a multi-DoF manipulation arm to the robot. [...]
Time-of-Flight Radiance Fields for Dynamic Scene View Synthesis
Abstract: Neural networks can represent and accurately reconstruct radiance fields for static 3D scenes (e.g., NeRF). Several works extend these to dynamic scenes captured with monocular video, with promising performance. However, the monocular setting is known to be an under-constrained problem, and so methods rely on data-driven priors for reconstructing dynamic content. We replace these [...]
Snakes & Spiders, Robots & Geometry
Abstract: Locomotion and perception are a common thread between robotics and biology. Understanding these phenomena at a mechanical level involves nonlinear dynamics and the coordination of many degrees of freedom. In this talk, I will discuss geometric approaches to organizing this information in two problem domains: Undulatory locomotion of snakes and swimmers, and vibration propagation [...]
Combining vision-based tactile, proximity, and global sensing for robotic manipulation
Abstract: I will begin by describing our work on visual servoing a manipulator and localizing objects using a robot-mounted suite of vision and vision-based tactile sensors, our results, algorithms used, and lessons learned. We show that by collocating tactile, and global (e.g. an RGB(D) camera) sensors, our setup can perform better than using each type [...]
Carnegie Mellon University
Visual Representation and Recognition without Human Supervision
Abstract: The advent of deep learning based artificial perception models has revolutionized the field of computer vision. These methods take advantage of the ever growing computational capacity of machines and the abundance of human-annotated data to build supervised learners for a wide-range of visual tasks. However, the reliance on human-annotated is also a bottleneck for [...]
Design, Modeling and Control for a Tilt-rotor VTOL UAV in the Presence of Actuator Failure
Abstract: Providing both the vertical take-off and landing capabilities and the ability to fly long distances to aircraft opens the door to a wide range of new real-world aircraft applications while improving many existing applications. Tiltrotor vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) are a better choice than fixed-wing and multirotor aircraft for [...]
Large Scale Dense 3D Reconstruction via Sparse Representations
Abstract: Scene reconstruction systems take in (3D) videos as input, and output 3D models with associated poses for inputs. With the demand of 3D content generation, the technique has been drastically evolving in recent years. For professionals equipped with depth sensors, efficient dense reconstruction systems have become available to efficiently recover scene geometry. For ordinary [...]
Carnegie Mellon University
Learning Multi-Modal Navigation in Unstructured Environments
Abstract: A robot that operates efficiently in a team with humans in an unstructured outdoor environment must translate commands into actions from a modality intuitive to its operator. The robot must be able to perceive the world as humans do so that the actions taken by the robot reflect the nuances of natural language and [...]
Lessons Learned from Creating Low-Cost Dexterous Soft Robot Hands
Abstract: Soft robot hands have shown promising results when it comes to dexterous grasping and manipulation. Compared to their rigid counterparts, soft hands can be manufactured for a fraction of the cost and offer robustness to uncertainty due to their inherent compliance. Unfortunately, the design and fabrication of soft robot hands is still a time-consuming [...]
Modern Trajectory Forecasting Methods Lack Social Awareness
Abstract: We present a thorough evaluation and analysis of state-of-the-art (SOTA) human trajectory forecasting methods with respect to metrics for safe and socially-aware prediction, e.g., collision rate, in addition to traditional displacement metrics, e.g., average displacement error. First, we introduce a system for trajectory classification which is used to evaluate the strengths and weaknesses of [...]