5 papers
TeFlow: Enabling Multi-frame Supervision for Self-Supervised Feed-forward Scene Flow Estimation
Qingwen Zhang, Chenhan Jiang, Xiaomeng Zhu +4
Self-supervised feed-forward methods for scene flow estimation offer real-time efficiency, but their supervision from two-frame point correspondences is unreliable and often breaks…
DIV-Nav: Open-Vocabulary Spatial Relationships for Multi-Object Navigation
Jesús Ortega-Peimbert, Finn Lukas Busch, Timon Homberger +2
Advances in open-vocabulary semantic mapping and object navigation have enabled robots to perform an informed search of their environment for an arbitrary object. However, such zer…
ViSA-Flow: Accelerating Robot Skill Learning via Large-Scale Video Semantic Action Flow
Changhe Chen, Quantao Yang, Xiaohao Xu +2
One of the central challenges preventing robots from acquiring complex manipulation skills is the prohibitive cost of collecting large-scale robot demonstrations. In contrast, huma…
FLoRA: Sample-Efficient Preference-based RL via Low-Rank Style Adaptation of Reward Functions
Daniel Marta, Simon Holk, Miguel Vasco +6
Preference-based reinforcement learning (PbRL) is a suitable approach for style adaptation of pre-trained robotic behavior: adapting the robot's policy to follow human user prefere…
One Map to Find Them All: Real-time Open-Vocabulary Mapping for Zero-shot Multi-Object Navigation
Finn Lukas Busch, Timon Homberger, Jesús Ortega-Peimbert +2
The capability to efficiently search for objects in complex environments is fundamental for many real-world robot applications. Recent advances in open-vocabulary vision models hav…