{"ID":2836349,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.21264","arxiv_id":"2511.21264","title":"Sampling-Based Optimization with Parallelized Physics Simulator for Bimanual Manipulation","abstract":"In recent years, dual-arm manipulation has become an area of strong interest in robotics, with end-to-end learning emerging as the predominant strategy for solving bimanual tasks. A critical limitation of such learning-based approaches, however, is their difficulty in generalizing to novel scenarios, especially within cluttered environments. This paper presents an alternative paradigm: a sampling-based optimization framework that utilizes a GPU-accelerated physics simulator as its world model. We demonstrate that this approach can solve complex bimanual manipulation tasks in the presence of static obstacles. Our contribution is a customized Model Predictive Path Integral Control (MPPI) algorithm, \\textbf{guided by carefully designed task-specific cost functions,} that uses GPU-accelerated MuJoCo for efficiently evaluating robot-object interaction. We apply this method to solve significantly more challenging versions of tasks from the PerAct$^{2}$ benchmark, such as requiring the point-to-point transfer of a ball through an obstacle course. Furthermore, we establish that our method achieves real-time performance on commodity GPUs and facilitates successful sim-to-real transfer by leveraging unique features within MuJoCo. The paper concludes with a statistical analysis of the sample complexity and robustness, quantifying the performance of our approach. The project website is available at: https://sites.google.com/view/bimanualakslabunitartu .","short_abstract":"In recent years, dual-arm manipulation has become an area of strong interest in robotics, with end-to-end learning emerging as the predominant strategy for solving bimanual tasks. A critical limitation of such learning-based approaches, however, is their difficulty in generalizing to novel scenarios, especially within...","url_abs":"https://arxiv.org/abs/2511.21264","url_pdf":"https://arxiv.org/pdf/2511.21264v1","authors":"[\"Iryna Hurova\",\"Alinjar Dan\",\"Karl Kruusamäe\",\"Arun Kumar Singh\"]","published":"2025-11-26T10:42:10Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","project_urls":"[\"https://sites.google.com/view/bimanualakslabunitartu\"]","has_code":false,"code_links":[{"ID":606592,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2836349,"paper_url":"https://arxiv.org/abs/2511.21264","paper_title":"Sampling-Based Optimization with Parallelized Physics Simulator for Bimanual Manipulation","repo_url":"https://github.com/google/safevalues","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0},{"ID":606593,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2836349,"paper_url":"https://arxiv.org/abs/2511.21264","paper_title":"Sampling-Based Optimization with Parallelized Physics Simulator for Bimanual Manipulation","repo_url":"https://github.com/AKS-Lab-Univertsity-of-Tartu/bimanual_manipulation","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}