6 papers
When Does Continual Learning Require Learning
Anne Harrington, Nayan Saxena, Michael Murphy +7
As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn? Today, the field largely frames this as a problem o…
T-Rex: Tactile-Reactive Dexterous Manipulation
Dantong Niu, Zhuoyang Liu, Zekai Wang +31
The ability to react dynamically to tactile signals has long been considered crucial to agile human-level dexterity. Yet contemporary learning-based Vision-Language-Action (VLA) mo…
Playful Agentic Robot Learning
Junyi Zhang, Jiaxin Ge, Hanjun Yoo +17
Current agentic robot systems can write executable Code-as-Policy programs, observe feedback, and revise behavior across multiple attempts, but they remain largely task-driven: reu…
Lifting Embodied World Models for Planning and Control
Alex N. Wang, Trevor Darrell, Pavel Izmailov +2
World models of embodied agents predict future observations conditioned on an action taken by the agent. For complex embodiments, action spaces are high-dimensional and difficult t…
Vibe Spaces for Creatively Connecting and Expressing Visual Concepts
Huzheng Yang, Katherine Xu, Andrew Lu +3
Creating new visual concepts often requires connecting distinct ideas through their most relevant shared attributes -- their vibe. We introduce Vibe Blending, a novel task for gene…
"I Know It When I See It": Mood Spaces for Connecting and Expressing Visual Concepts
Huzheng Yang, Katherine Xu, Michael D. Grossberg +2
Expressing complex concepts is easy when they can be labeled or quantified, but many ideas are hard to define yet instantly recognizable. We propose a Mood Board, where users conve…