RoboPapers

Geeking out with Yixuan & Siyuan on CLONE:Closed-Loop Whole-Body Humanoid Teleoperation for Long-Horizon Taskshttps://humanoid-clone.github.ioCo-hosted by Chris Paxton & Michael Cho

Geeking out with Yuanhang Zhang on FALCON - Learning Force-Adaptive Humanoid Loco-Manipulation https://lecar-lab.github.io/falcon-humanoid/Co-hosted by Chris Paxton & Michael Cho

Geeking out with Tony Tao & Mohan Kumar Srirama on https://dexwild.github.ioCo-hosted by Michael Cho & Chris Paxton

In this engaging discussion, Haoyu Zheng, a PhD student at UMass Amherst focusing on 3D foundational models, dives into his groundbreaking work on 4D embodied world models called TesserAct. He explores how these models can predict future states and generate photorealistic robotic simulations. Haoyu reveals insights on the complexities of training these models, including pre-training effectiveness and the challenges of zero-shot learning. The conversation highlights the importance of data quality and advancements in video diffusion models for the future of robotics.

Arthur Allshire and Hongsuk Choi, both PhD students at UC Berkeley, dive into their groundbreaking project, VideoMimic. They discuss how humanoid robots can learn locomotion and interaction through human imitation. Key insights include advancements in 3D reconstruction from videos, the challenges of kinematic retargeting, and the integration of depth mapping technologies. They also touch on the complexity of training robots in diverse environments and the exciting potential of using minimal video data for effective robotic training.

In this engaging discussion, Harald Schäfer leads the autonomy team at Comma AI, sharing insights from his eight-year journey in robotics. He dives into groundbreaking advancements in self-driving technology, emphasizing data-driven learning and world models. The conversation covers the challenges of developing versatile systems for various car models and innovative simulation strategies. Harald also explores the trade-offs in world model training, the importance of harnessing human-driven data, and the commitment to open-source innovations in automotive AI that could revolutionize user experiences.

Join Vincent Liu, a Stanford math and computer science graduate, and Ademi Adeniji, a UC Berkeley PhD student, as they dive into their groundbreaking work on robot learning through smart glasses. They discuss the vital role of human interaction in enhancing robot intelligence and the challenges of mapping movements to robotic actions. The duo also tackles the costs and benefits of advanced smart glasses, along with the complexities of using third-party data for accurate robotic learning. Tune in for insights into robotic evolution and future tech innovations!

Yanjie Ze, a first-year PhD student at Stanford, dives into the innovative TWIST system, enhancing humanoid robot capabilities through teleoperation. The discussion reveals how human data dramatically improves robot dexterity and addresses challenges in lower body tracking. Yanjie explains the significance of large-scale motion datasets for refining robot movements and explores the complexities of control frameworks. The conversation also highlights advancements in teleoperated robotics, focusing on latency improvements and the potential of full-body engagement for enhanced robotic performance.

Emil Barr, a fresh PhD graduate and researcher at FAIR, dives into the fascinating world of navigation in robotics. He discusses the shift from traditional mathematical models to cutting-edge neural networks that help robots navigate dynamic environments. Topics include the complexities of training world models with observational data, the innovative use of conditional diffusion transformers for state prediction, and the role of advanced trajectory generation methods. It's a thrilling exploration of how AI is shaping the future of robotics!

In this enlightening discussion, Xuesu Xiao, an assistant professor of computer science at George Mason University and a roboticist, shares insights on advanced robotic motion planning. He delves into the innovative Vertiformer model designed for navigating difficult terrains, emphasizing the fusion of physics and machine learning. The conversation highlights challenges in estimating flight durations, the importance of data efficiency in transformers, and the integration of real-world testing for enhancing vehicle performance. Get ready to explore the future of robotics!