Student Talks
Safely Influencing Humans in Human-Robot Interaction
Abstract: Robots are becoming more common in industrial manufacturing because of their speed and precision on repetitive tasks, but they lack the flexibility of human collaborators. In order to take advantage of both humans’ and robots’ abilities, we investigate how to improve the efficiency of human-robot collaborations by making sure that robots both 1. stay [...]
Inductive Biases for Learning Long-Horizon Manipulation Skills
Abstract: Enabling robots to execute temporally extended sequences of behaviors is a challenging problem for learned systems, due to the difficulty of learning both high-level task information and low-level control. In this talk, I will discuss three approaches that we have developed to address this problem. Each of these approaches centers on an inductive bias [...]
Analogy-Forming Transformers for Few-Shot 3D Parsing
Abstract: How do we build agents that can fast generalize to novel scenarios given only a single example? In this talk, I will present analogy-forming transformers, a semi-parametric model that segments 3D object scenes by retrieving related memories and predicting analogous part structures for the input. This enables a single neural network to continually learn [...]
Range-based Gaussian Process Maps for Mobile Exploration Robots
Abstract: Mobile robots exploring unknown, natural environments with limited communication must map their surroundings using onboard sensors. In this context, terrain mapping can rely on Gaussian process models to incorporate spatial correlations and provide uncertainty estimates when predicting ground height - however, these models fail to account for the oblique viewpoint of a sensor on [...]
Learning Exploration Strategies to Solve Real-World Marble Runs
Abstract: Tasks involving locally unstable or discontinuous dynamics (such as bifurcations and collisions) remain challenging in robotics, because small variations in the environment can have a significant impact on task outcomes. In this talk, we present a robot system that we developed to evaluate learning algorithms on real-world physical problem solving tasks which incorporate these [...]
Beyond NeRF Underwater: Learning Neural Reflectance Fields for True Color Correction of Marine Imagery
Abstract: Underwater imagery often exhibits distorted coloration as a result of light-water interactions, which complicates the study of benthic environments in marine biology and geography. In this research, we propose an algorithm to restore the true color (albedo) in underwater imagery by jointly learning the effects of the medium and neural scene representations. Our approach [...]
Carnegie Mellon University
Optimization of Small Unmanned Ground Vehicle Design using Reconfigurability, Mobility, and Complexity
Abstract: Unmanned ground vehicles are being deployed in increasingly diverse and complex environments. With modern developments in sensing and planning, the field of ground vehicle mobility presents rich possibilities for mechanical innovations that may be especially relevant for unmanned systems. In particular, reconfigurability may enable vehicles to traverse a wider set of terrains with greater [...]
Force-Torque Sensors – Calibration & Estimation
Abstract: Wrist force-torque sensors were among the first proprioception sensors to be developed when robotics emerged as a field. They are now a mature technology already used in structured industrial applications like sanding and drilling. While they provide essential feedback in many manipulation algorithms, they do not garner as much excitement as exteroception sensors like [...]
Optimized Tradeoffs for Differentially Private Majority Ensembling
Abstract: Inspired by the common subtask of ensembling or calibrating private models, we study the problem of computing an m*epsilon-differentially private majority of K epsilon-differentially private algorithms for m < K. We introduce a general framework to compute the private majority via Randomized Response (RRM) with a data-dependent noise function gamma that subsumes any non-trivial [...]
Carnegie Mellon University
Spectral Mapping using Simple Sensors for Micro-Explorers
Abstract: Spectral mapping is an essential task in exploration as it expands our understanding of material composition in an explored region. Although imaging spectrometers are ideal for obtaining spectra to construct spectral maps, their large size, high power consumption, and operational complexity make them impractical for small rovers and limited missions. In contrast, RGB cameras [...]
Simulation-driven vision-based tactile sensor design using Physics Based Rendering
Abstract: Touch is an essential sensing modality for making autonomous robots more dexterous and works collaboratively with humans. With the advent of vision-based tactile sensors, roboticists have tried to incorporate tactile sensors in various robot structures for various robotic manipulation tasks to increase robustness, precision, and reliability. However, the design of vision-based tactile sensors is [...]
Efficient Interactive Learning with Unobserved Confounders
Abstract: Interactive learning systems like self-driving cars, recommender systems, and large language model chatbots are becoming increasingly ubiquitous in everyday life. From a machine learning perspective, the key technical challenge underlying such systems is that rather than simple prediction on i.i.d. data, an interactive learner influences the distribution of inputs it sees via the choices [...]
Carnegie Mellon University
Towards Reconstructing Non-rigidity from Single Camera
Abstract: In this talk we will discuss how to infer 3D from images captured by a single camera, without assuming the target scenes / objects being static. The non-static setting makes our problem ill-posed and challenging to solve, but is vital in practical applications where target-of-interest is non-static. To solve ill-posed problems, the current trend [...]
MSR Thesis Talk: Sarvesh Patil
Title: Soft Delta Robots for Dexterous Manipulation Abstract: Dexterous manipulation capabilities of end-effectors afford us a wide range of strategies for fine-grained manipulation tasks. Recent utilization of readily available materials like soft filaments and silicone elastomers has enabled the development of low-cost mechanically intelligent robotic manipulators. This is important for democratizing robot manipulation and increasing [...]
Large Scale Dense 3D Reconstruction via Sparse Representations
Abstract: Dense 3D scene reconstruction is in high demand today for view synthesis, navigation, and autonomous driving. A practical reconstruction system inputs multi-view scans of the target using RGB-D cameras, LiDARs, or monocular cameras, computes sensor poses, and outputs scene reconstructions. These algorithms are computationally expensive and memory-intensive due to the presence of 3D data. [...]
MSR Thesis Talk: Fan Yang
Title: Exploring Safe Reinforcement Learning for Sequential Decision Making Abstract: Safe Reinforcement Learning (RL) focuses on the problem of training a policy to maximize the reward while ensuring safety. It is an important step towards applying RL to safety-critical real-world applications. However, safe RL is challenging due to the trade-off between the two objectives [...]
Incorporating Robustness into Learning-Based Aircraft Detection and Tracking Systems
Abstract: In the field of aviation, the Detect and Avoid (DAA) problem deals with incorporating collision avoidance capabilities into current autopilot navigation systems. In order to standardize DAA capabilities, ASTM has published performance requirements to define safe DAA operations of unmanned aircraft systems (UAS). However, the performance of DAA models are entirely dependent on the [...]
Carnegie Mellon University
MSR Thesis Talk: Siddarth Venkatraman
Title: Latent Skill Models for Offline Reinforcement Learning Abstract: Offline reinforcement learning (RL) holds promise as a means to learn high-value policies from a static dataset, without the need for further environment interactions. However, a key challenge in offline RL lies in effectively stitching portions of suboptimal trajectories from the static dataset while avoiding extrapolation [...]
Long-Tailed 3D Detection via Multi-Modal Fusion
Abstract: Contemporary autonomous vehicle (AV) benchmarks have advanced techniques for training 3D detectors, particularly on large-scale LiDAR data. Surprisingly, although semantic class labels naturally follow a long-tailed distribution, these benchmarks focus on only a few common classes (e.g., pedestrian and car) and neglect many rare classes in-the-tail (e.g., debris and stroller). However, in the real [...]
MSR Thesis Talk: Eric Schneider
Title: Phenotyping and Skeletonization for Agricultural Robotics Abstract: Scientific phenotyping of plants is a crucial aspect of experimental plant breeding. By accurately measuring plant characteristics, phenotyping plays a vital role in the development of new plant varieties that are better adapted to specific environments and have improved yield, quality, and resistance to stress and disease. In [...]
MSR Thesis Talk: Shivesh Khaitan
Zoom Link: https://cmu.zoom.us/j/95273358670?pwd=Z09Jc3g1aDV1dTdTMEVUWUwxcUZPQT09 Meeting ID: 952 7335 8670 Passcode: 050721 Title: Exploring Reinforcement Learning approaches for Safety Critical EnvironmentsAbstract: Reinforcement Learning (RL) has emerged as a powerful paradigm for addressing challenging decision-making and robotic control tasks. By leveraging the principles of trial-and-error learning, RL algorithms enable agents to learn optimal strategies through interactions with an environment. However, [...]
MSR Thesis Talk: Ravi Tej Akella
Title: Distributional Distance Classifiers for Goal-Conditioned Reinforcement Learning Abstract: Autonomous systems are increasingly being deployed in stochastic real-world environments. Often, these agents are trying to find the shortest path to a commanded goal. But what does it mean to find the shortest path in stochastic environments, where every strategy has a non-zero probability of failing? At [...]
MSR Thesis Talk: Seth Karten
Title: Emergent Communication and Decision-Making in Multi-Agent Teams Abstract: Explicit communication among humans is key to coordinating and learning. In multi-agent reinforcement learning for partially-observable environments, agents may convey information to others via learned communication, allowing the team to complete its task. However, agents need to be able to communicate more than simply referential messages [...]
MSR Thesis Talk: Sashank Tirumala
Title: Tactile Sensing applied to deformable object manipulation Abstract: The application of robotic manipulation of deformable materials, such as cloth, spans various sectors including fabric manufacturing and domestic laundry management. Historically, most methodologies have employed vision-based sensors as the proprioceptive input to robot policies. However, this study aims to explore an alternate route by leveraging [...]