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LEGS: Fine-Tuning Teleop-Free VLAs for Humanoid Loco-manipulation in an Embodied Gaussian Splatting World
Hojune Kim, Timothy Chen, Jiankai Sun +4
Training vision-language-action (VLA) policies for humanoid loco-manipulation is constrained by the high cost and complexity of collecting human teleoperation demonstrations. VLA p…
GRaD-Nav++: Vision-Language Model Enabled Visual Drone Navigation with Gaussian Radiance Fields and Differentiable Dynamics
Qianzhong Chen, Naixiang Gao, Suning Huang +4
Autonomous drones capable of interpreting and executing high-level language instructions in unstructured environments remain a long-standing goal. Yet existing approaches are const…
Semantic-Metric Bayesian Risk Fields: Learning Robot Safety from Human Videos with a VLM Prior
Timothy Chen, Marcus Dominguez-Kuhne, Aiden Swann +2
Humans interpret safety not as a binary signal but as a continuous, context- and spatially-dependent notion of risk. While risk is subjective, humans form rational mental models th…
GRaD-Nav: Efficiently Learning Visual Drone Navigation with Gaussian Radiance Fields and Differentiable Dynamics
Qianzhong Chen, Jiankai Sun, Naixiang Gao +3
Autonomous visual navigation is an essential element in robot autonomy. Reinforcement learning (RL) offers a promising policy training paradigm. However existing RL methods suffer…
VISTA: Open-Vocabulary, Task-Relevant Robot Exploration with Online Semantic Gaussian Splatting
Keiko Nagami, Timothy Chen, Javier Yu +5
We present VISTA (Viewpoint-based Image selection with Semantic Task Awareness), an active exploration method for robots to plan informative trajectories that improve 3D map qualit…
HAMMER: Heterogeneous, Multi-Robot Semantic Gaussian Splatting
Javier Yu, Timothy Chen, Mac Schwager
3D Gaussian Splatting offers expressive scene reconstruction, modeling a broad range of visual, geometric, and semantic information. However, efficient real-time map reconstruction…