RI Seminar

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 [...]

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 [...]

Abstract: Achieving safety, scalability, and high performance in complex systems, such as multi-agent systems (MAS) control, is a central challenge in many real-world robotic deployments due to its computational complexity as a large-scale constrained optimal control problem. To address this, we introduce a novel graph control barrier function (GCBF) as a core tool for large-scale [...]

Abstract: The talk will revisit my career in manipulation research, focusing on projects that might offer some useful lessons for others. We will start with my beginnings at the MIT AI Lab and my MS thesis, which is still my most cited work, then continue with my arrival at CMU, a discussion with Allen Newell, [...]

Abstract:  Dexterous manipulation is a grand challenge of robotics, and fine manipulation skills are required for many robotics applications that we envision.   In this overview talk, I will discuss my view of some major factors that contribute to dexterity and discuss how we can incorporate them into our robots and systems. Bio:  Nancy Pollard [...]

Abstract: In an era of rapid AI progress, leveraging accelerated computing and big data has unlocked new possibilities to develop generalist AI models. As AI systems like ChatGPT showcase remarkable performance in the digital realm, we are compelled to ask: Can we achieve similar breakthroughs in the physical world — to create generalist humanoid robots capable [...]

Abstract: In the age of deep networks, "learning" almost invariably means "learning from examples". We train language models with human-generated text and labeled preference pairs, image classifiers with large datasets of images, and robot policies with rollouts or demonstrations. When human learners acquire new concepts and skills, we often do so with richer supervision, especially [...]