Despite recent advances in humanoid locomotion, controllers optimized for command tracking and robustness tend to produce mechanical gaits, whereas controllers tied to human motion data often fail to generalize to commands outside the data distribution. This work introduces a learning framework that balances these comp...
M. Zhang, Dong-Ho Kang, Kevin Bergamin et al.· 0 citations
The ability to efficiently acquire generalized skills from demonstrations and apply them across diverse real-world scenarios is a central challenge in robot manipulation. Unlike conventional robot learning tasks that rely on extensive action demonstrations for single-task performance, zero-shot manipulation demands th...
Collecting high-quality robot data for contact-rich manipulation tasks is essential for enabling robots to acquire real-world skills. However, existing data collection solutions often lack the capability to obtain stable and high-frequency tactile feedback, limiting their effectiveness in contact-rich manipulation scen...
Xiwen Dengxiong, Xue-Ting Wang, Ke Jing et al.· 0 citations
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