{"slug": "neuro-inspired-inverse-learning-for-planning-and-control", "title": "Neuro-Inspired Inverse Learning for Planning and Control", "summary": "Researchers led by Tonio Ball introduced the Inverter framework, a neuro-inspired approach for embodied planning and control that uses paired forward/inverse internal models and open-loop multi-step motor commands, trained end-to-end through a new method called Inverse Learning (IL). On all 3 maze2d and 6 antmaze D4RL benchmarks, single Inverters or hierarchical n=2 stacks matched or improved on offline-RL and diffusion-planner baselines by an average of +24.2% (range -1.9% to +78.2%) while using one to two orders of magnitude less inference compute time. The framework also demonstrated a Pulse Inverter that synthesizes arbitrary single-qubit quantum gates with fidelity matching the GRAPE baseline at over 1000x lower per-gate compute time, though the authors identified a failure mode called FoM hacking under narrow training-data coverage.", "body_md": "# Computer Science > Artificial Intelligence\n\n[Submitted on 22 May 2026 (\n\n[v1](https://arxiv.org/abs/2605.24152v1)), last revised 26 May 2026 (this version, v2)]# Title:Neuro-Inspired Inverse Learning for Planning and Control\n\n[View PDF](/pdf/2605.24152)\n\n[HTML (experimental)](https://arxiv.org/html/2605.24152v2)\n\nAbstract:We present a neuro-inspired framework for embodied planning and control. Building on three principles that enable fast and highly effective goal-directed behavior in the mammalian brain - paired forward/inverse internal models, open-loop multi-step motor commands, and sequential, hierarchical organization of action - our Inverter framework uses learned components, trained end-to-end through Inverse Learning (IL) and supplemented where natural by analytic or algorithmic modules; we formalize IL and delineate it from supervised, reinforcement, and imitation learning. IL bridges Reinforcement Learning (RL)-style amortization, which runs in a single forward pass but emits only one action at a time, and Optimal Control (OC)-style sequence planning over whole trajectories, but with iterative test-time computation. Single Inverters or hierarchical n=2 Inverter stacks match or improve on offline-RL and diffusion-planner baselines on all 3 maze2d and 6 antmaze D4RL variants by an average of +24.2% (range -1.9% to +78.2%), at one-to-two orders of magnitude less inference compute time. Distinctively, optimizing through the Forward Model (FoM) over the entire T-step action sequence - rather than per step - lets Inverters produce smooth, goal-coherent, trajectory-wide structure and reach control policies closer to the analytic optimum than the policy underlying the training data itself. We also identify a failure mode of IL: FoM hacking under narrow training-data coverage, which we mitigate by using random training data with broader coverage. As an application example, a Pulse Inverter synthesizes arbitrary single-qubit quantum gates with fidelity matching the standard iterative numerical baseline (GRAPE), at more than 1000x lower per-gate compute time. In summary, we conclude that IL enables a versatile class of world-interfaces, especially for latency- and resource-critical embodied AI.\n\n## Submission history\n\nFrom: Tonio Ball [[view email](/show-email/347a3e87/2605.24152)]\n\n**Fri, 22 May 2026 19:19:32 UTC (4,100 KB)**\n\n[[v1]](/abs/2605.24152v1)**[v2]** Tue, 26 May 2026 06:41:34 UTC (4,100 KB)\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/neuro-inspired-inverse-learning-for-planning-and-control", "canonical_source": "https://arxiv.org/abs/2605.24152", "published_at": "2026-07-31 18:10:46+00:00", "updated_at": "2026-07-31 18:22:25.026750+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "ai-research", "ai-infrastructure"], "entities": ["Tonio Ball", "Inverter", "Inverse Learning", "D4RL", "GRAPE", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/neuro-inspired-inverse-learning-for-planning-and-control", "markdown": "https://wpnews.pro/news/neuro-inspired-inverse-learning-for-planning-and-control.md", "text": "https://wpnews.pro/news/neuro-inspired-inverse-learning-for-planning-and-control.txt", "jsonld": "https://wpnews.pro/news/neuro-inspired-inverse-learning-for-planning-and-control.jsonld"}}