Abstract:  Human beings sense and express themselves through multiple modalities. To capture multimodal ways of human communication, I want to build adaptable robot policies that infer task pragmatics from video and language prompts, reason about sounds and other sensors, take actions, and learn mannerisms of interacting with people and objects. Existing solutions for robot policies [...]

Abstract: The integration of soft and multifunctional materials in emerging technologies is becoming more widespread due to their ability to enhance or improve functionality in ways not possible using typical rigid alternatives. This trend is evident in various fields. For example, wearable technologies are increasingly designed using soft materials to improve modulus compatibility with biological [...]

Abstract: With modern robotic technologies, seafloor imageries have become more accessible to both researchers and the public. This thesis leverages deep learning and 3D vision techniques to deliver valuable information from seafloor image observations. Despite the widespread use of deep learning and 3D vision algorithms across various fields, underwater imaging presents unique challenges, such as [...]

Abstract: A long-standing problem in autonomous driving is grappling with the long-tail of rare scenarios for which little or no data is available. Although learning-based methods scale with data, it is unclear that simply ramping up data collection will eventually make this problem go away. Approaches which rely on simulation or world modeling offer some [...]

Abstract: As humans, we are constantly interacting with and observing a three-dimensional dynamic world; where objects around us change state as they move or are moved, and we, ourselves, move for navigation and exploration. Such an interaction between a dynamic environment and a dynamic ego-agent is complex to model as an ego-agent's perception of the [...]

Abstract: Since infancy, humans acquire motor skills, behavioral priors, and objectives by learning from their caregivers. Similarly, as we create humanoids in our own image, we aspire for them to learn from us and develop universal physical and cognitive capabilities that are comparable to, or even surpass, our own. In this thesis, we explore how [...]

Abstract: While Generative AI has shown breakthroughs in recent years in generating new digital contents such as images or 3D models from high-level goal inputs like text, Robotics technologies have not, instead focusing on low-level goal inputs. We propose Generative Robotics, as a new field of robotics which combines the high-level goal input abilities of [...]

Abstract: 3D scene understanding from 2D video is essential for enabling advanced applications such as autonomous driving, robotics, virtual reality, and augmented reality. These fields rely on accurate 3D spatial awareness and dynamic interaction modeling to navigate complex environments, manipulate objects, and provide immersive experiences. Unlike 2D, 3D training data is much less abundant, which [...]

Abstract: The goal of this thesis is to discover AI processes that enhance cross-domain and cross-task generalization in intelligent robot agents. Unlike the dominant approach in contemporary robot learning, which pursues generalization primarily through scaling laws (increasing data and model size), we focus on identifying the best abstractions and representations in both perception and policy [...]

Abstract: Modern computer vision has achieved remarkable success in tasks such as detecting, segmenting, and estimating the pose of humans in images and videos, reaching or even surpassing human-level performance. However, they still face significant challenges in predicting and analyzing future human motion. This thesis explores how vision-based solutions can enhance the fidelity and accuracy [...]

Abstract: Mobile robots used for information gathering tasks rely on dense, predictive mapping of large-scale regions to determine where to take measurements. Current approaches to mapping commonly rely on Gaussian process regression to spatially correlate data, extrapolate from sparse samples, and estimate uncertainty. However, these approaches do not incorporate meaningful information about physical processes that [...]

Abstract: With the popularity of Virtual Reality (VR), Augmented Reality (AR), and other 3D applications, developing methods that let everyday users capture and create their own 3D content has become increasingly essential. Current 3D creation pipelines often require either tedious manual effort or specialized setups with densely captured views. Additionally, many resulting 3D models are [...]

Abstract: A new category of computing devices is emerging: augmented and virtual reality headsets, collectively referred to as extended reality (XR). These devices can alter, augment, or even replace our reality. While these headsets have made impressive strides in audio-visual immersion over the past half-century, XR interactions remain almost completely absent of appropriately expressive tactile [...]

Abstract: In this talk, we present our work on advancing Vision-Language Models (VLMs) beyond scaling laws through improved evaluation and (post-)training strategies. Our contributions include VQAScore, a state-of-the-art alignment metric for text-to-visual generation. We show how VQAScore improves visual generation under real-world user prompts in GenAI-Bench. Additionally, we explore training methods that leverage the language [...]

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, [...]

Abstract: Large text-to-image models learn from training data to synthesize "novel" images, but how the models use the training data remains a mystery. The problem of data attribution is to identify which training images are influential for generating a given output. Specifically, removing influential images and retraining the model would prevent it from reproducing that [...]

Abstract: This thesis explores diverse decision-making strategies for autonomous agents by examining knowledge-dependent and data-dependent approaches in stationary and dynamic data environments. We address five core research problems across three thematic areas: knowledge-dependent, stationary data-dependent, and evolving data-dependent decision-making. We first investigate knowledge-driven decision-making within robotic swarms, characterizing vulnerabilities in systems governed by consistent rule-following [...]

Abstract: Ensuring pedestrian safety in dynamic environments is a key challenge for autonomous systems, particularly in dynamic, multi-agent environments. Trajectory forecasting plays a central role in enabling these systems to anticipate pedestrian behaviors and respond appropriately. This thesis addresses three core limitations in trajectory forecasting systems which impede safe and robust trajectory forecasting: inadequate evaluation protocols [...]

Abstract: Generative models have advanced significantly, synthesizing photorealistic images, videos, and text. Building on this progress, our work explores the potential of diffusion models to bridge generative and discriminative learning, uncovering new pathways for leveraging their strengths in visual perception tasks. In the first part, we propose Diff-2-in-1, a unified framework for multi-modal data generation [...]

Abstract: Uncrewed Aerial Vehicles (UAVs) have attracted the interest of researchers, industry, and the general public in many applications. Noticing that high-altitude tasks sometimes require active interaction with the environment, there have been more and more works focusing on aerial manipulation recently. Each of them has demonstrated the ability to use a specific aerial manipulator [...]