RI Seminar

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 [...]

Abstract:  Large-scale robot fleets are increasingly deployed in warehouses, factories, transportation systems, and emerging robotics applications. Coordinating hundreds or thousands of robots in shared, cluttered spaces creates fundamental challenges in maintaining safety, preventing deadlocks, and minimizing congestion. In this talk, I will present our recent work on scalable imitation learning methods for coordinating 10k robots, automatic environment [...]

Abstract: For intelligent robots to become ubiquitous, we need to “solve" locomotion, navigation and manipulation at sufficient reliability in widely varying environments. In locomotion, we now have demonstrations of humanoid walking in a variety of challenging environments.  In navigation, we pursued the task of “Go to Any Thing” – a robot, on entering  a newly [...]

Abstract: In the last century, it was common to envision robots as shining metal structures with rigid and halting motion. This imagery is in contrast to the fluid and organic motion of living organisms that inhabit our natural world. The adaptability, complex control, and advanced learning capabilities observed in animals are not yet fully understood, [...]

Abstract: Moravec’s Paradox observes that AI systems have struggled far more with learning physical actions than symbolic reasoning. Yet just recently, there has been a tremendous increase in the capability of AI-driven robotic systems, reminiscent  of the early improvements in language modeling capabilities a few years ago.  In this talk, we provide mathematical evidence that learning in continuous-control [...]

Abstract: Formal methods - mathematical techniques for describing systems, capturing requirements, and providing guarantees - have been used to synthesize robot control from high-level specification, and to verify robot behavior. Given the recent advances in robot learning and data-driven models, what role can, and should, formal methods play in advancing robotics? In this talk I [...]

Abstract: How can we build robots that meaningfully assist people with mobility limitations in their daily lives? To support complex caregiving tasks such as robot-assisted feeding, bathing, transferring, and meal preparation, robots must physically interact with people and objects in dynamic, unstructured environments while maintaining safety. In this talk, I will present an overview of [...]

Abstract: The classical approach to AI designed systems that were rational at run-time: they had explicit representations of beliefs, goals, and plans and ran inference algorithms, online, to select actions. The rational approach was criticized (by the behaviorists) and modified (by the probabilists) but persisted in some form. More recently, relatively unstructured data-driven end-to-end approaches [...]