Carnegie Mellon University
Design and Validation of Minimally-Actuated Mobility for Planetary Rovers
Abstract: This thesis investigates low degree-of-freedom (DoF), minimally-actuated rover mobility architectures for planetary exploration. These platforms seek to reduce mass and complexity compared to the heritage rocker-bogie system while maintaining functional performance. The work focuses on two systems: MoonRanger, a lightweight lunar micro-rover slated for a 2029 moon mission, and Zoë2, a research rover developed [...]
Whole-Body Conditioned Egocentric Video Prediction
Abstract: We train models to Predict Ego-centric Video from human Actions (PEVA), given the past video and an action represented by the relative 3D body pose. By conditioning on kinematic pose trajectories, structured by the joint hierarchy of the body, our model learns to simulate how physical human actions shape the environment from a first-person [...]
Getting Optimization layers to play well with Deep Networks : Numerical methods and Architectures
Abstract: Many real-world challenges, from robotic control to resource management, can be effectively formulated as optimization problems. Recent advancements have focused on incorporating these optimization problems as layers within deep learning pipelines, enabling the explicit inclusion of auxiliary constraints or cost functions, which is crucial for applications such as enforcing physical laws, ensuring safety constraints, [...]
Scaling Laws Revisited: When Data, Not Compute, is the Bottleneck
Abstract: The formula for AI progress has long appeared straightforward: more compute, more data, better models. Yet while compute is growing with better hardware and bigger clusters, data is stagnating—calling into question the very scaling laws that powered the last decade. The internet—often described as the “fossil fuel” of AI—offers only a finite reservoir of [...]
Accessible Dexterous Manipulation with Soft Hands: Designs, Methods, Models, and the DexKit Platform
Abstract: Robot dexterity remains an open challenge in robotics that has the potential to transform manufacturing, healthcare, and daily life. Robots that safely and robustly interact with unstructured environments must combine compliant hardware with models and planners that tolerate uncertainty. Additionally, if robust robot dexterity is to be realized outside of research labs, it must [...]
Designing Community: Co-Designed Sensing, Haptics, and Frugal Robotics for the Public Good
Abstract: This talk presents a portfolio of community‑partnered research that uses co‑design to turn local needs into embodied technologies. I will share case studies that span environmental justice, accessibility, and disaster response. First, an intergenerational air‑quality program where community members learn programming, soldering, open‑source tools, and electrical engineering while building and then deploying DIY air‑quality [...]
Biologically Inspired Soft Robotics
Abstract: Robotics has the potential to address many of today’s pressing problems in fields ranging from healthcare to manufacturing to disaster relief. However, the traditional approaches used on the factory floor do not perform well in unstructured environments. The key to solving many of these challenges is to explore new, non-traditional designs. Fortunately, nature surrounds [...]
Robust Incremental Distributed Collaborative Simultaneous Localization and Mapping
Abstract: Multi-robot teams show exceptional promise across applications like Search-and-Rescue, disaster-response, agriculture, forestry, and scientific exploration due to their ability to go where humans cannot, parallelize activity, operate robustly to failures, and expand capabilities beyond that of an individual robot. Collaborative Simultaneous Localization and Mapping (C-SLAM) is a fundamental capability for these multi-robot teams as [...]
From Sparse to Dense, and Back to Sparse Again?
Abstract: Computer vision architectures used to be built on a sparse sample of points in the 80s and 90s. In the 2000s, dense models started to become popular for visual recognition as heuristically defined sparse models do not cover all the important parts of an image. However, with deep learning and end-to-end training approaches, this does [...]
Towards 4D perception with foundational priors
Abstract: As humans, we are constantly interacting with and observing a three-dimensional dynamic world. Building this spatiotemporal or 4D understanding in vision algorithms is not straightforward as there is orders of magnitude less 4D data than 2D images and videos. This underscores the need to find meaningful ways to exploit 2D data to realize 4D [...]
Using Embodied Agents to Reverse-Engineer Natural Intelligence
Abstract: Modern AI faces (at least!) two challenges: (1) building agents capable of autonomy and life-long learning, and (2) embodying them to perform these tasks in the real-world. In this talk, I will discuss our approach to these questions, and show that they also are tightly intertwined with reverse-engineering brains across multiple species, from rodents [...]