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Earlier sources: AI and robotics
Robotics combines physical machines with sensing, planning, and control. This AI thread follows learning-based robot policies, simulation, perception, and evaluation in physical environments.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗41 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- arXiv · Published · First archived
FORM: Robot Manipulation through Direct Material Law Identification ↗
When interacting with an unfamiliar deformable material, a robot lacks prior knowledge of its physical properties and how it will respond to applied forces and motion. Rapid online identification is therefore essential for reliable manipulation. We present FORM (From Observed Response to Material laws), which identifies material properties from a single robot interaction and reuses the recovered model to plan manipulation under new actions and geometries. We use weak-form momentum balance to convert observed material motion and contact forces into linear equations in the unknown material param…
- arXiv · Published · First archived
SafeVantage: Vantage-Aware Memory for Reliable Embodied Decisions ↗
Reliable embodied decisions under partial observability require informative observations and sufficient supporting evidence. However, semantic scores alone do not reveal which viewpoints justify a claim or where additional evidence should be acquired. We introduce SafeVantage, a vantage-aware semantic memory and active acquisition framework that retains each claim's supporting views, camera poses, and estimated target location, keeping positive support distinct from search coverage. A learned candidate-observability model uses claim-grounded geometry to predict target visibility at reachable v…
- GitHub · Repository created · First archived
12-Twelve-12/robolings ↗
Small exercises for robot learning, rustlings style: kinematics, dexterous hands, control, diffusion policy, flow matching. NumPy only.
- arXiv · Published · First archived
Test-Time Spatial Reasoning for Robot Manipulation Using Generative Real-to-Sim ↗
Spatial reasoning is fundamental to general robot intelligence, as it enables robots to complete long-horizon tasks involving multi-object interaction. We introduce Simify, a training-free, test-time framework that performs explicit spatial reasoning via massively parallel physics simulation. From a single RGB-D image of a scene, Simify reconstructs simulation-ready assets leveraging 3D generative models and vision-language models. Then given a task specified by a reward function (e.g., build the tallest tower), Simify launches thousands of parallel rollouts in simulation and performs an evolu…
- arXiv · Published · First archived
Nutri-ATLAS: Embodied Agent for Tabulated Lookup and Assistance for Smarter nutrition ↗
Generative and Agentic IoT systems offer a promising foundation for digital healthcare applications that combine sensing, personalized reasoning, and autonomous interaction in real-world environments. Nutrition assistance is a natural use case, but existing Large Language Model (LLM)-based systems are often limited to passive text interaction and static context, making them unreliable when food descriptions are ambiguous or nutritional evidence is missing. We propose Nutri-ATLAS, an Embodied Agent for Tabulated Lookup and Assistance for smarter nutrition in the real world. It integrates graph-…
- arXiv · Published · First archived
PlanGuard: A Guardrail for Multi-Step Plan Safety in Embodied Agents ↗
Embodied task planners may produce multi-step plans whose subtask dependencies and interactions with the environment create physical risks during execution. Yet existing safeguards overlook such compositional risks, as general-purpose guardrails focus on semantic harm and embodied safety detectors assess subtasks in isolation. To address this gap, we introduce PlanGuard, the first pre-execution detector that evaluates the physical safety of a complete multi-step plan in its current environment. For training and evaluation, we construct a Multi-Step Plan Safety (MSP-Safe) dataset through paired…
- OpenAlex · Published · First archived
Graph Neural Network‐Based Reinforcement Learning for Decentralized Multi‐Robot Manipulation ↗
In tightly cooperative manipulation tasks, robotic manipulators must follow collision‐free and coordinated trajectories. Existing multiagent learning frameworks often rely on centralized planners that provide strong coordination but fail to scale with larger teams. Alternatively, decentralized approaches offer better scalability but typically lack communication, which limits their ability to achieve highly cooperative behaviors. To address this gap, this paper proposes a graph neural network (GNN)‐based framework for scalable multiagent reinforcement learning (RL). In our formulation, each man…
- arXiv · Published · First archived
Learning Robot Policies from Sparse Success Signals via STL-Guided Stein Variational Policy Gradient ↗
Learning robot policies for tasks with sparse success signals is challenging when completion depends on coordinated actions, precise contact outcomes, or satisfying several conditions together. Intricate physical interactions with the world further complicate these requirements. Prior work using conventional reward shaping mechanisms provides dense feedback but local progress might not translate into eventual task completion. We present Signal Temporal Logic-guided Stein Variational Policy Gradient (STL-SVPG), a population-based method that uses smooth STL robustness as a trajectory-level trai…
- arXiv · Published · First archived
Representation-Guided Generation and Integration of Executable Programs for Robot Manipulation ↗
Building a robotic manipulation system requires connecting perception, planning, and control through carefully designed representations and interfaces. VLM code generation offers a way to automate this construction, but independently generated components may operate on incompatible geometric and task-level information. We present Representation-guided Integration of VLM-generated Executable Task programs (RIVET), a framework for generating complete manipulation systems around a shared object-centric representation. The representation combines per-object 6D poses, which preserve the metric info…
- arXiv · Published · First archived
Cybflight: An Embedded Rust Autopilot for Aerial Robotics Research ↗
Bringing an aerial robotics method from simulation to flight should not require rebuilding a mature autopilot or adding a companion computer. Cybflight is an open-source embedded Rust research autopilot whose typed, replaceable interfaces connect hardware access, perception, state estimation, trajectory planning, and control. This modular development and compile-time optimization workflow is demonstrated with replaceable Rust implementations of model predictive contour-tracking control (MPCTC) and incremental nonlinear dynamic inversion (INDI) running on one STM32H743 without a companion compu…
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How to assess evidence about AI and robotics
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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