aiqbench analysis
This research introduces Language-conditioned Heat-inspired Diffusion (LHD), a novel framework that combines vision-language priors with heat-diffusion kernels to enable efficient multi-robot motion planning. By treating collision avoidance as a physical inductive bias, the method eliminates the need for explicit obstacle maps during inference while maintaining high success rates on real robots. The approach significantly reduces computational latency and improves generalization across out-of-distribution scenarios compared to standard diffusion-based planners.
Multi-Robot Motion Planning from Vision and Language using Heat-Inspired Diffusion
arXiv:2512.13090v2 Announce Type: replace Abstract: Diffusion models have recently emerged as powerful tools for robot motion planning by capturing the multi-modal distribution of feasible trajectories. However, their extension to multi-robot settings with flexible, language-conditioned task specifications remains limited. Furthermore, current diffusion-based approaches incur high computational cost during inference and struggle with generalization because they require explicit construction of environment representations and lack mechanisms for reasoning about geometric reachability. To address these limitations, we present Language-conditioned Heat-inspired Diffusion (LHD), an end-to-end vision-based framework that generates language-conditioned, collision-free trajectories. LHD integrates semantic priors from CLIP, a vision-language model (VLM), with a collision-avoiding diffusion kernel serving as a physical inductive bias that enables the planner to interpret language commands strictly within the reachable workspace. This naturally handles out-of-distribution (OOD) scenarios -- in terms of reachability -- by guiding robots toward accessible alternatives that match the semantic intent, while eliminating the need for explicit obstacle information at inference time. Extensive evaluations on diverse real-world-inspired maps, along with real-robot experiments, show that LHD consistently outperforms prior diffusion-based planners in success rate, while reducing planning latency. Project page is available at: https://jebeom.github.io/lhd_project_page/
Abstract:Diffusion models have recently emerged as powerful tools for robot motion planning by capturing the multi-modal distribution of feasible trajectories. However, their extension to multi-robot settings with flexible, language-conditioned task specifications remains limited. Furthermore, current diffusion-based approaches incur high computational cost during inference and struggle with generalization because they require explicit construction of environment representations and lack mechanisms for reasoning about geometric reachability. To address these limitations, we present Language-conditioned Heat-inspired Diffusion (LHD), an end-to-end vision-based framework that generates language-conditioned, collision-free trajectories. LHD integrates semantic priors from CLIP, a vision-language model (VLM), with a collision-avoiding diffusion kernel serving as a physical inductive bias that enables the planner to interpret language commands strictly within the reachable workspace. This naturally handles out-of-distribution (OOD) scenarios -- in terms of reachability -- by guiding robots toward accessible alternatives that match the semantic intent, while eliminating the need for explicit obstacle information at inference time. Extensive evaluations on diverse real-world-inspired maps, along with real-robot experiments, show that LHD consistently outperforms prior diffusion-based planners in success rate, while reducing planning latency. Project page is available at: this https URL
Robotics (cs.RO)
arXiv:2512.13090 [cs.RO]
(or arXiv:2512.13090v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2512.13090 Focus to learn more arXiv-issued DOI via DataCite
IEEE Robotics and Automation Letters, vol. 11, no. 6, pp. 7118-7125, June 2026
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Full text under the publisher's open licence · 287 words
