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cs.RO2026
Expanding Spatial and Temporal Context for Robotic Imitation Learning With Scene Graphs
Jianing Qian, Qinhe Peng, Emmanuel Panov +4
Imitation learning enables robots to learn how to execute tasks via observation. However, real-world environments like homes and offices are often severely partially observed due t…
cs.RO2026
Tether: Autonomous Functional Play with Correspondence-Driven Trajectory Warping
William Liang, Sam Wang, Hung-Ju Wang +3
The ability to conduct and learn from interaction and experience is a central challenge in robotics, offering a scalable alternative to labor-intensive human demonstrations. Howeve…
cs.RO2025
RICL: Adding In-Context Adaptability to Pre-Trained Vision-Language-Action Models
Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman +1
Multi-task ``vision-language-action'' (VLA) models have recently demonstrated increasing promise as generalist foundation models for robotics, achieving non-trivial performance out…