AI RESEARCH
ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation
arXiv CS.LG
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ArXi:2506.04646v3 Announce Type: replace-cross Planning with learned dynamics models offers a promising approach toward versatile real-world manipulation, particularly in nonprehensile settings such as pushing or rolling, where accurate analytical models are difficult to obtain. However, collecting