Tactile feedback provides embodied agents with physical information beyond visual observations, enabling more reliable interaction with the real world. However, despite the rapid progress of vision-tactile-language-action (VTLA) policies, there remains a lack of unified benchmarks for evaluating tactile-enabled robot m...
World-action models guide action generation with predicted future observations, but vision-centric predictions miss the local contact cues that decide contact-rich manipulation. However, naively predicting future tactile observations as additional views recovers only a third of the achievable gain in our experiments. T...
Zi-Pei Ma, Xiao-Fei Wei, Jun-Zhe Jiang et al.· 2 citations
This work introduces RoboSkill, a framework that connects skill acquisition and reuse through an Explore, Execute, Evolve loop, and complements vision with tactile feedback to reduce uncertainty during physical interaction and augment textual guidance with reusable code to reduce reasoning overhead during skill reuse.
Si-Cheng Xie, Yi-Tong Chen, Hai-Dong Cao et al.· 0 citations
This work revisits the scaling recipe for BFMs and demonstrates that substantial performance gains can be achieved through the coordination of three core components: the learning paradigm of motion tracking that reformulates diverse humanoid control problems as the reproduction of integrated whole-body behaviors in the...
