Student Talks
Perception for High-Speed Off-Road Driving
Abstract: On-road autonomous driving has seen rapid progress in recent years with driverless vehicles being tested in various cities worldwide. However, this progress is limited to cities with well-established infrastructure and has yet to transfer to off-road regimes with unstructured environments and few paved roads. Advances in high-speed and reliable autonomous off-road driving can unlock [...]
Continual Learning of Compositional Skills for Robust Robot Manipulation
Abstract: Real world robots need to continuously learn new manipulation tasks in a lifelong learning manner. These new tasks often share sub-structures (in the form of sub-tasks, controllers) with previously learned tasks. To utilize these shared sub-structures, we explore a compositional and object-centric approach to learn manipulation tasks. While compositionality in robot manipulation can manifest [...]
Carnegie Mellon University
MSR Thesis Talk: Samuel Ong
Title: Data-Driven Slip Model for Improved Localization and Path Following applied to Lunar Micro-Rovers Abstract Micro-lunar rovers need to solve a slew of challenges on the Moon, with no human intervention. One such challenge is the need to know their location in order to navigate and build maps. However, localization is challenging on the moon due [...]
Computational Interferometric Imaging
Abstract: Imaging systems typically accumulate photons that, as they travel from a light source to a camera, follow multiple different paths and interact with several scene objects. This multi-path accumulation process confounds the information that is available in captured images about the scene and makes using these images to infer properties of scene objects, such [...]
Robust and Context-Aware Real-Time Collaborative Robot Handling with Dynamic Gesture Commands
Abstract: Real-time collaborative robot (cobot) handling is a task where the cobot maneuvers an object under human dynamic gesture commands. Enabling dynamic gesture commands is useful when the human needs to avoid direct contact with the robot or the object handled by the robot. However, the key challenge lies in the heterogeneity in human behaviors [...]
Equivalent Policy Sets for Learning Aligned Models and Abstractions
Abstract: Recent successes in model-based reinforcement learning (MBRL) have demonstrated the enormous value that learned representations of environmental dynamics (i.e., models) can impart to autonomous decision making. While a learned model can never perfectly represent the dynamics of complex environments, models that are accurate in the "right” ways may still be highly useful for decision [...]
Dynamic Route Guidance in Vehicle Networks by Simulating Future Traffic Patterns
Abstract: Roadway congestion leads to wasted time and money and environmental damage. Since adding more roadway capacity is often not possible in urban environments, it is becoming more important to use existing road networks more efficiently. Toward this goal, recent research in real-time, schedule-driven intersection control has shown an ability to significantly reduce the delays [...]
Adaptive Robotic Assistance through Observations of Human Behavior
Abstract: Assistive robots should take actions that support people's goals. This is especially true as robots enter into environments where personal agency is paramount, such as a person's home. Home environments have a wide variety of "optimal' solutions that depend on personal preference, making it difficult for a robot to know the goal it should [...]
Beyond Pick-and-Place: Towards Dynamic and Contact-rich Motor Skills with Reinforcement Learning
Abstract: Interactions with the physical world are at the core of robotics. However, robotics research, especially in manipulation, has been mainly focused on tasks with limited interactions with the physical world such as pick-and-place or pushing objects on the table top. These interactions are often quasi-static, have predefined or limited sequence of contact events and [...]
Adaptive-Anytime Planning and Mapping for Multi-Robot Exploration in Large Environments
Abstract: Robotic systems are being leveraged to explore environments too hazardous for humans to enter. Robot sensing, compute, and kinodynamic (SCK) capabilities are inextricably tied to the size, weight, and power (SWaP) constraints of the vehicle. When designing a robot team for exploration, the diversity and types of robots used must be carefully considered because [...]
Neural Radiance Fields with LiDAR Maps
Abstract: Maps, as our prior understanding of the environment, play an essential role for many modern robotic applications. The design of maps, in fact, is a non-trivial art of balance between storage and richness. In this thesis, we explored map compression for image-to-LiDAR registration, LiDAR-to-LiDAR map registration, and image-to-SfM map registration, and finally, inspired by [...]
Enabling Data-Efficient Real-World Model-Based Manipulation by Estimating Preconditions for Inaccurate Models
Abstract: This thesis explores estimating and reasoning about model deviation in robot learning for manipulation to improve data efficiency and reliability to enable real-robot manipulation in a world where models are inaccurate but still useful. Existing strategies are presented for improving planning robustness with low amounts of real-world data by an empirically estimated model precondition to guide [...]
Robust Adaptive Reinforcement Learning for Safety Critical Applications via Curricular Learning
Abstract: Reinforcement Learning (RL) presents great promises for autonomous agents. However, when using robots in a safety critical domain, a system has to be robust enough to be deployed in real life. For example, the robot should be able to perform across different scenarios it will encounter. The robot should avoid entering undesirable and irreversible [...]
MSR Thesis Talk: Yichen Li
Title: Simulation-guided Design for Vision-based Tactile Sensing on a Soft Robot Finger Abstract: Soft pneumatic robot manipulators have garnered widespread interest due to their compliance and flexibility, which enable soft, non-destructive grasping and strong adaptability to complex working environments. Tactile sensing is crucial for these manipulators to provide real-time contact information for control and manipulation. [...]
Controllable Visual-Tactile Synthesis
Abstract: Deep generative models have various content creation applications such as graphic design, e-commerce, and virtual Try-on. However, current works mainly focus on synthesizing realistic visual outputs, often ignoring other sensory modalities, such as touch, which limits physical interaction with users. The main challenges for multi-modal synthesis lie in the significant scale discrepancy between vision [...]
Perceiving Particles Inside a Container using Dynamic Touch Sensing
Abstract: Dynamic touch sensing has shown potential for multiple tasks. In this talk, I will present how we utilize dynamic touch sensing to perceive particles inside a container with two tasks: classification of the particles inside a container and property estimation of the particles inside a container. First, we try to recognize what is inside [...]
Towards Photorealistic Dynamic Capture and Animation of Human Hair and Head
Abstract: Realistic human avatars play a key role in immersive virtual telepresence. To reach a high level of realism, a human avatar needs to faithfully reflect human appearance. A human avatar should also be drivable and express natural motions. Existing works have made significant progress on building drivable realistic face avatars, but they rarely include [...]
Carnegie Mellon University
System Identification and Control of Multiagent Systems Through Interactions
Abstract: This thesis investigates the problem of inferring the underlying dynamic model of individual agents of a multiagent system (MAS) and using these models to shape the MAS's behavior using robots extrinsic to the MAS. We investigate (a) how an observer can infer the latent task and inter-agent interaction constraints from the agents' motion and [...]
Examining the Role of Adaptation in Human-Robot Collaboration
Abstract: Human and AI partners increasingly need to work together to perform tasks as a team. In order to act effectively as teammates, collaborative AI should reason about how their behaviors interplay with the strategies and skills of human team members as they coordinate on achieving joint goals. This talk will discuss a formalism for [...]
A Multi-view Synthetic and Real-world Human Activity Recognition Dataset
Abstract: Advancements in Human Activity Recognition (HAR) partially relies on the creation of datasets that cover a broad range of activities under various conditions. Unfortunately, obtaining and labeling datasets containing human activity is complex, laborious, and costly. One way to mitigate these difficulties with sufficient generality to provide robust activity recognition on unseen data is [...]
Eye Gaze for Intelligent Driving
Abstract: Intelligent vehicles have been proposed as one path to increasing vehicular safety and reduce on-road crashes. Driving intelligence has taken many forms, ranging from simple blind spot occupancy or forward collision warnings to lane keeping and all the way to full driving autonomy in certain situations. Primarily, these methods are outward-facing and operate on [...]
Dense 3D Representation Learning for Geometric Reasoning in Manipulation Tasks
Abstract: When solving a manipulation task like "put away the groceries" in real environments, robots must understand what *can* happen in these environments, as well as what *should* happen in order to accomplish the task. This knowledge can enable downstream robot policies to directly reason about which actions they should execute, and rule out behaviors [...]
Passive Coupling in Robot Swarms
Abstract: In unstructured environments, ant colonies demonstrate remarkable abilities to adaptively form functional structures in response to various obstacles, such as stairs, gaps, and holes. Drawing inspiration from these creatures, robot swarms can collectively exhibit complex behaviors and achieve tasks that individual robots cannot accomplish. Existing modular robot platforms that employ dynamic coupling and decoupling [...]
Learning novel objects during robot exploration via human-informed few-shot detection
Abstract: Autonomous mobile robots exploring in unfamiliar environments often need to detect target objects during exploration. Most prevalent approach is to use conventional object detection models, by training the object detector on large abundant image-annotation dataset, with a fixed and predefined categories of objects, and in advance of robot deployment. However, it lacks the capability [...]
Learning to Perceive and Predict Everyday Interactions
Abstract: This thesis aims to develop a computer vision system that can understand everyday human interactions with rich spatial information. Such systems can benefit VR/AR to perceive the reality and modify its virtual twin, and robotics to learn manipulation by watching human. Previous methods have been limited to constrained lab environment or pre-selected objects with [...]
Learning Models and Cost Functions from Unlabeled Data for Off-Road Driving
Abstract: Off-road driving is an important instance of navigation in unstructured environments, which is a key robotics problem with many applications, such as exploration, agriculture, disaster response and defense. The key challenge in off-road driving is to be able to take in high dimensional, multi-modal sensing data and use it to make intelligent decisions on [...]
Active Vision for Manipulation
Abstract: Decades of research on computer vision has highlighted the importance of active sensing -- where the agent actively controls parameters of the sensor to improve perception. Research on active perception the context of robotic manipulation has demonstrated many novel and robust sensing strategies involving a multitude of sensors like RGB and RGBD cameras, a [...]
Continually Improving Robots
Abstract: General purpose robots should be able to perform arbitrary manipulation tasks, and get better at performing new ones as they obtain more experience. The current paradigm in robot learning involves training a policy, in simulation or directly in the real world, with engineered rewards or demonstrations. However, for robots that need to keep learning [...]
Carnegie Mellon University
Parallelized Search on Graphs with Expensive-to-Compute Edges
Abstract: Search-based planning algorithms enable robots to come up with well-reasoned long-horizon plans to achieve a given task objective. They formulate the problem as a shortest path problem on a graph embedded in the state space of the domain. Much research has been dedicated to achieving greater planning speeds to enable robots to respond quickly [...]