Complex whole-body loco-manipulation requires executing diverse maneuvers, such as crouching, grasping, and locomotion, within a single long-horizon task. While performing simple pick-and-place behaviors is well-established, scaling to complex, long-horizon tasks remains difficult. Currently, this challenge is addresse...
S. Manni, Annika Wollschläger, Dario Bellicoso et al.· 0 citations
This framework enables versatile humanoid mobility in challenging environments by using motion data strictly as a training prior for natural whole-body coordination while retaining the flexibility to discover environment-dependent behaviors through reinforcement learning.
Jun-Zhe He, Ke-Han Wen, Chong Zhang et al.· 0 citations
Learning from demonstration (LfD) has enabled humanoid robots to acquire diverse whole-body skills, but extending this paradigm to human-object interaction (HOI) is limited by the availability of robot-compatible interaction references. We present HOI-Retarget, a contact-centric retargeting method that transfers HOI on...
Jih-Wan Shin, Adrià López Escoriza, Jun-Zhe He et al.· 2 citations
This work uses Sample-based Model Predictive Control entirely in simulation as an automated, rapidly tunable expert to generate massive offline datasets and validate the robustness of this sim-to-real framework by successfully deploying complex loco-manipulation skills across different morphologies.
Martin Schuck, Maks Sorokin, S. Manni et al.· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
