Learning Environment Models for Mobile Robot Autonomy
Abstract: Robots are expected to execute increasingly complex tasks in increasingly complex and a priori unknown environments. A key prerequisite is the ability to understand the geometry and semantics of the environment in real time from sensor observations. This talk will present techniques for learning metric-semantic environment models from RGB and depth observations. Specific examples include [...]
Teruko Yata Memorial Lecture in Robotics
Title: Learning World Simulators from Data Abstract: Modern foundational models have achieved superhuman performance in many logic and mathematical reasoning tasks by learning to think step by step. However, their ability to understand videos, and, consequently, control embodied agents, lags behind. They often make mistakes in recognizing simple activities, and often hallucinate when generating videos. This [...]
Investigating Compositional Reasoning in Time Series Foundation Models
Abstract: Large pre-trained time series foundation models (TSFMs) have demonstrated promising zero-shot performance across a wide range of domains. However, a question remains: Do TSFMs succeed solely by memorizing training patterns, or do they possess the ability to reason? While reasoning is a topic of great interest in the study of Large Language Models (LLMs), [...]
Learning Efficient 3D Generation
Abstract: Recent advances in 3D generation have enabled the synthesis of multi-view images using large-scale pre-trained 2D diffusion models. However, these methods typically require dozens of forward passes, resulting in significant computational overhead. In this talk, we introduce Turbo3D, an ultra-fast text-to-3D system that generates high-quality Gaussian Splatting assets in under one second. Turbo3D features a [...]
Reconstructing Tree Skeletons in Agricultural Robotics: A Comparative Study of Single-View and Volumetric Methods
Abstract: This thesis investigates the problem of reconstructing tree skeletons for agricultural robotics, comparing single-view image-based (Image to 3D) and volumetric (3D to 3D) methods. Accurate 3D modeling is essential for robotic tasks like pruning and harvesting, where understanding the underlying branch structure is critical. Using a custom-generated dataset of synthetic trees, we train encoder-decoder [...]
Faculty Candidate Talk: Aja Carter
Title: Paleorobotics: Design Principles 540 million years in the making Abstract: Bioinspiration has provided key design insights in many fields, particularly in robotics, where there has been an explosion of interest in quadrupedal robot “dogs” and bipedal humanoid robots. However, the designs prescribed by only considering living animals are a small subset of available designs; [...]
Faculty Candidate Talk: Carlo Sferrazza
Title: The Path to Humanoid Intelligence Abstract: Humanoid robots represent the ideal physical embodiment to assist us in the diversity of our daily tasks and human-centric environments. Driven by substantial hardware advancements, progress in artificial intelligence (AI), and a growing demand for adaptable automation, this vision appears increasingly feasible. Yet, to date, humanoid intelligence remains [...]
Physical Intelligence and Cognitive Biases Toward AI
Abstract: When will robots be able to clean my house, dishes, and take care of laundry? While we source labor primarily from automated machines in factories, the penetration of physical robots in our daily lives has been slow. What are the challenges in realizing these intelligent machines capable of human level skill? Isn’t AI advanced [...]
Robotics Institute Semi-formal
Hello all Robotics Institute faculty, students, visitors and staff, You and a guest are cordially invited to attend The Robotics Institute Semi-formal
Faculty Candidate Talk: Jason Ma
Title: Internet Supervision for Robot Learning Abstract: The availability of internet-scale data has led to impressive large-scale AI models in various domains, such as vision and language. For learning robot skills, despite recent efforts in crowd-sourcing robot data, robot-specific datasets remain orders of magnitude smaller. Rather than focusing on scaling robot data, my research takes the alternative path of directly [...]
