The spiking neural network-based Proximal Policy Optimization algorithm integrates the use of spike-based actor-critic reinforcement learning with the Proximal Policy Optimization algorithm and uses the stochastic Gaussian policy in the autonomous navigation of unmanned aerial vehicles.
F. Walugembe, Maciej Wielgosz, Tomaž Goričan et al.· 0 citations
This work introduces RiverVLN, to its knowledge the first benchmark designed for long-horizon USV VLN under continuous riverine motion, and PGT-NAV, a phase-grounded temporal navigation framework for USVs.
Jie-Ling Wu, Yue-Hao Huang, Jia-Jun Lv et al.· 0 citations
DiagGen is presented, an agentic framework that turns a single in-the-wild image into a simulation-ready deformable asset through a generate--simulate--diagnose--refine loop, and shows that diagnostics provides useful repair cues and can moderately improve the quality of generated deformable assets.
Guan-Xiong Chen, Yi-Duo Qu, Qian-Jun Xia et al.· 1 citation
In this paper, we investigate the Minimum Obstacle Displacement Planning problem from a robot motion planning perspective. The problem involves determining a feasible path to a goal location by displacing movable obstacles when no collision-free path initially exists. We show that this problem is computationally challe...
Antony Thomas, Giulio Ferro, Fulvio Mastrogiovanni et al.· 0 citations
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This review offers a unified synthesis of collaboration architectures and topologies, neural-communication co-design that treats the channel as a differentiable pipeline component, embodied action-perception loops via multi-agent reinforcement learning, and resilience mechanisms for synchronization, uncertainty quantif...
Lei Zhang, Chun-Lu Ye, Le Yang et al.· Research· 0 citations
We present \emph{commonsense ranked search} (CoRS), a novel path planner that turns an abstract instruction into a route that follows commonsense. While existing methods respect the considerations written down in advance, a robot working among people must follow those left unstated too, as with a wet floor that a worke...
HIGenNTO is presented, a framework that synthesizes humanoid-scene interaction motion references by optimizing the initial noise of a pretrained text-conditioned motion model under sparse spatiotemporal and scene constraints, establishing a scalable path from high-level task descriptions to physically executable humano...
Insertion is a fundamental operation in robotic construction assembly, where variations in material properties and assembly conditions make it difficult to select contact forces that complete the task without exceeding the assembly's capacity. Although construction documents encode engineering knowledge about materials...
Lin-Jin He, Yan-Yi Chen, Hao Sun et al.· 0 citations
Large language model (LLM) planners can decompose natural-language instructions and select reusable robot skills, but choosing the correct skill does not guarantee successful physical execution. This gap is especially important in humanoid loco-manipulation, where errors during approach, grasping, transport, or placeme...
Ajay Vikram Periasami, Xin Luo, Hao-Yu Li et al.· 0 citations
AffordanceWAM is introduced, an affordance-aware generative World Action Model that represents object-centric spatiotemporal affordance through Scalar Affordance and Affordance Heatmap, within the generated future World, and supports affordance as an effective interface for both vision-language-action learning and huma...
Order (Ontology-driven Decision-making for Embodied Reasoning), a benchmark built on a fictitious world: a 342,069-token synthetic corpus defining a self-consistent physics that cannot appear in any model's pre-training data, shows evidence of genuine world-model induction rather than memorization.
The proposed prototype validates the feasibility of deploying privacy-preserving, edge-native assistive intelligence for cost-sensitive mobility assistance on an ultra-low-power Raspberry Pi Zero 2W.
Ali Akarma, Adeel Ahmad, Toqeer Ali Syed· arXiv.org· 0 citations
A new system identifies and corrects unreliable object descriptions stored in a robot’s memory, improving the robot’s ability to interact with its environment.
Robots are getting smarter, but how can their hardware match that growth? New Microsoft Research findings show that moving AI inference beyond the robot can improve task success, boost efficiency, and support more advanced physical AI workloads. The post Offloaded inference for real-world physical AI robotics appeared first on Microsoft Research.
