VASC Seminar
Vishnu Lokhande
Assistant Professor
University at Buffalo, SUNY

Creating robust deep learning models involves effectively managing nuisance variables

Newell-Simon Hall 3305

Abstract: Over the past decade, we have witnessed significant advances in capabilities of deep neural network models in vision and machine learning. However, issues related to bias, discrimination, and fairness in general, have received a great deal of negative attention (e.g., mistakes in surveillance and animal-human confusion of vision models). But bias in AI models [...]

MSR Thesis Defense
MSR Student
Robotics Institute,
Carnegie Mellon University

Transfer Learning via Temporal Contrastive Learning Inbox

GHC 4405

Abstract: This thesis introduces a novel transfer learning framework for deep reinforcement learning. The approach automatically combines goal-conditioned policies with temporal contrastive learning to discover meaningful sub-goals. The approach involves pre-training a goal-conditioned agent, finetuning it on the target domain, and using contrastive learning to construct a planning graph that guides the agent via sub-goals. Experiments [...]

PhD Thesis Proposal
PhD Student
Robotics Institute,
Carnegie Mellon University

Towards Influence-Aware Safe Human-Robot Interaction

NSH 3305

Abstract: In recent years, we have seen through recommender systems on social media how influential (and potentially harmful) algorithms can be in our lives, sometimes creating polarization and conspiracies that lead to unsafe behavior. Now that robots are also growing more common in the real world, we must be very careful to ensure that they [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Learning to Manipulate beyond Imitation

NSH 3002

Abstract: Imitation learning has been a prevalent approach for teaching robots manipulation skills but still suffers from scalability and generalizability. In this talk, I'll argue for going beyond elementary behavioral imitation from human demonstrations. Instead, I'll present two key directions: 1) Creating Manipulation Controllers from Pre-Trained Representations, and 2) Representing Video Demonstrations with Parameterized Symbolic [...]

Faculty Events
Assistant Professor
Robotics Institute,
Carnegie Mellon University

Advanced robotics for manufacturing: challenges and opportunities

Newell-Simon Hall 4305

Abstract: Presenting projects with ARM Institute (including robot grinding, human-robot collaboration, and modularized manufacturing) and discussing some new opportunities in applying AI and robotics in manufacturing domain.

PhD Thesis Defense
Extern
Robotics Institute,
Carnegie Mellon University

Improving Robot Capabilities Through Reconfigurability

GHC 6501

Abstract: Advancements in robot capabilities are often achieved through integrating more hardware components. These hardware additions often lead to systems with high power consumption, fragility, and difficulties in control and maintenance. However, is this approach the only path to enhancing robot functionality? In this talk, I introduce the PuzzleBots, a modular multi-robot system with passive [...]

Faculty Events

RI Faculty Business Meeting

Newell-Simon Hall 4305

Meeting for RI Faculty. Discussions include various department topics, policies, and procedures. Generally meets weekly.

RI Seminar
Krzysztof Skonieczny
Associate Professor
Electrical and Computer Engineering, Concordia University

Reduced-Gravity Flights and Field Testing for Lunar and Planetary Rovers

1305 Newell Simon Hall

Abstract: As humanity returns to the Moon and is developing outposts and related infrastructure, we need to understand how robots and work machines will behave in this harsh environment. It is challenging to find representative testing environments on Earth for Lunar and planetary rovers. To investigate the effects of reduced-gravity on interactions with granular terrains, [...]

PhD Thesis Proposal
PhD Student
Robotics Institute,
Carnegie Mellon University

Design Principles for Robotics Systems that Support Human-Human Collaborative Learning

GHC 6121

Abstract: Robots possess unique affordances granted by combining software and hardware. Most existing research focuses on the impact of these affordances on human-robot collaboration, but the theory of how robots can facilitate human-human collaboration is underdeveloped. Such theory would be beneficial in education. An educational device can afford collaboration in both assembly and use. This [...]