Self-supervised Learning and Generalization
Abstract: Contrastive self-supervised learning is a highly effective way of learning representations that are useful for, i.e. generalise, to a wide range of downstream vision tasks and datasets. In the first part of the talk, I will present MoCHi, our recently published contrastive self-supervised learning approach (NeurIPS 2020) that is able to learn transferable representations [...]
Carnegie Mellon University
Teleoperation via Intuition: Safe and Intent Oriented Navigation
Abstract: This thesis aims to enable seamless teleoperation of a mobile robot by a human operator, such that the robot navigates in unstructured environments following the operator’s intent intuitively, safely, and efficiently. The roles of the human and robot are disproportionate in traditional teleoperation: The human is responsible for most of the autonomy of the [...]
Enabling Robots to Cooperate & Compete: Distributed Optimization & Game Theoretic Methods for Multiple Interacting Robots
Abstract: For robots to effectively operate in our world, they must master the skills of dynamic interaction. Autonomous cars must safely negotiate their trajectories with other vehicles and pedestrians as they drive to their destinations. UAVs must avoid collisions with other aircraft, as well as dynamic obstacles on the ground. Disaster response robots must coordinate [...]
Learning to see from few labels
Abstract: Computer vision systems today exhibit a rich and accurate understanding of the visual world, but increasingly rely on learning on large labeled datasets to do so. This reliance on large labeled datasets is a problem especially when one considers difficult perception tasks, or novel domains where annotations might require effort or expertise. We thus [...]
Carnegie Mellon University
Towards the Automated Design of Neural Networks
Abstract: Neural architecture search (NAS) is recently proposed to automate the process of designing network architectures. Instead of manually designing network architectures, NAS automatically finds the optimal architecture in a data-driven way. Despite its impressive progress, NAS is still far from being widely adopted as a common paradigm for architecture design in practice. This thesis [...]
The Role of Manipulation Primitives in Building Dexterous Robotic Systems
Abstract: I will start this talk by illustrating four different perspectives that we as a community have embraced to study robotic manipulation: 1) controlling a simplified model of the mechanics of interaction with an object; 2) using haptic feedback such as force or tactile to control the interaction with an environment; 3) planning sequences or [...]
Seeing the unseen: inferring unobserved information from multi-modal data
Abstract: As humans we can never fully observe the world around us and yet we are able to build remarkably useful models of it from our limited sensory data. Machine learning problems are often required to operate in a similar setup, that is the one of inferring unobserved information from the observed one. Partial observations [...]
Carnegie Mellon University
Analysis of Deadlock in Multirobot Systems
Abstract: Collision avoidance for multirobot systems is a well-studied problem. Recently, control barrier functions (CBFs) have been proposed for synthesizing controllers that guarantee safety while simultaneously encouraging goal stabilization for multiple robots. However, it has been noted that reactive control synthesis methods (such as CBFs) are prone to deadlock, an equilibrium of system dynamics that [...]
Design and Analysis of Open-Source Educational Haptic Devices
Abstract: The sense of touch (haptics) is an active perceptual system used from our earliest days to discover the world around us. However, formal education is not designed to take advantage of this sensory modality. As a result, very little is known about the effects of using haptics in K-12 and higher education or the [...]
Towards AI for 3D Content Creation
Abstract: 3D content is key in several domains such as architecture, film, gaming, and robotics. However, creating 3D content can be very time consuming -- the artists need to sculpt high quality 3d assets, compose them into large worlds, and bring these worlds to life by writing behaviour models that "drives" the characters around in [...]
Move over, MSE! – New probabilistic models of motion
Abstract: Data-driven character animation holds great promise for games, film, virtual avatars and social robots. A "virtual AI actor" that moves in response to intuitive, high-level input could turn 3D animators into directors, instead of requiring them to laboriously pose the character for each frame of animation, as is the case today. However, the high [...]