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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 ↗39 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Recent 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.
- Hacker News · Posted on Hacker News · First archived
Aspire: Agentic /Skills Discovery for Robotics ↗
Hacker News discussion.
- arXiv · Published · First archived
Value as Memory: Online Value-Guided Post-training for General Robot Policies ↗
Real-world post-training of generalist robot policies faces two challenges: partial observability makes action selection ambiguous, while offline post-training methods delay feedback from deployment experience. We present Value as Memory (VaM), a framework that addresses these challenges through value caching and online real-world reinforcement learning (RL). Value caching conditions actions on recent value predictions as task-progress memory, while online updates enable timely policy improvement by combining offline demonstrations, off-policy interventions, and on-policy rollouts for sample-e…
- arXiv · Published · First archived
Spark-0: Scaling Spatially Aligned Tactile Pretraining for Robot Dexterous Manipulation ↗
Generalist robot policies benefit from large-scale visual pretraining but often struggle with contact-rich dexterous manipulation, where force, compliance, and incipient slip are hard to observe from images. We present Spark-0, a spatially aligned vision-tactile-language-action (VTLA) model that uses touch in two complementary ways. During pretraining, image-space contact maps derived from tactile gloves, together with features from a frozen 3D geometry teacher, supervise the visual tokens; these objectives are removed at inference. In parallel, a tactile pathway encodes regional force histori…
- Hacker News · Posted on Hacker News · First archived
PCEI – On-device embodied AI combining BitNet 1.58B, Mamba, and FastKAN ↗
Hacker News discussion.
- arXiv · Published · First archived
Physical Time as a Missing Dimension of Robot Policy Memory Dynamics ↗
Existing robot policy memories typically evolve with observation sequences, implicitly treating each update as the same amount of physical time. Yet identical observation histories can span different durations and require different actions, exposing physical time as a missing dimension of policy memory. We introduce Temporal Flow Policy (TFP), which learns memory dynamics to retain past events and evolve the memory state over measured time intervals. Observations and prior memory determine the content and time scales of each transition; elapsed time determines how far the state advances. TFP c…
- OpenAlex · Published · First archived
Data-driven static position modeling of a parallel tendon-actuated soft continuum robot using artificial neural networks ↗
Abstract Parallel soft continuum robots combine the compliance and adaptability of soft structures with the structural advantages of parallel mechanisms, offering promising capabilities for complex robotic applications. However, their highly nonlinear behavior presents significant challenges for accurate and computationally efficient modeling. This study presents a data-driven approach for static end-effector position modeling of a parallel tendon-actuated soft continuum robot using an artificial neural network (ANN). A physics-based model of the robot is developed in the SoRoSim MATLAB framew…
- OpenAlex · Published · First archived
A Two-Stage Neural Network Framework for Swimming Direction Control of an Underwater Walking Robot: Kinematic Validation via Simulation and In-Air Experiments ↗
Each year, hundreds of thousands of tons of derelict fishing gear become entangled in ship propellers, and resolving such incidents currently requires divers to manually cut the entangled material underwater, exposing them to the risk of secondary accidents. To address this problem, we developed a radial quadruped underwater robot that walks along the hull of a ship using electromagnetic feet and swims freely using propellers mounted on the distal link of each leg. However, because each leg has only four degrees of freedom (DOFs), the platform is an underactuated system that cannot independent…
- Hugging Face · Model / dataset date · First archived
nvidia/PhysicalAI-Robotics-PhysicalAssets-VoMP — dataset update ↗
# VoMP: Predicting Volumetric Mechanical Properties ## Dataset Description: The Pre-Processed 3D Dataset is a dataset that is composed of 4 individual 3D asset datasets which are processed to render them from multiple views, voxelize the assets, and propagate VLM annotations for material properties. We release pre-processed data derived from the 3D assets, specifically: voxels, rendered images, and LLM-annotated material descriptions. This dataset is for research and development only. ## Dataset Owner(s): NVIDIA Corporation ## Dataset Creation Date: 27 October 2025 ## License/Terms of Use: Thi
- arXiv · Published · First archived
ChunkAct: Action Chunking for Agentic Robotics ↗
Agentic robotics places a large language model (LLM) agent in the robot control loop, where it composes perception, motion, and vision-language-action (VLA) tools under the guidance of a memory and thereby generalizes to new tasks without retraining. This generality comes at a steep price in efficiency. Every primitive requires a round of LLM reasoning over the full interaction history, so the cost of an episode grows with the number of primitives. We find this reasoning largely redundant: once a sub-goal is chosen, its primitives follow a routine already prescribed by memory. We present Chunk…
- arXiv · Published · First archived
Who Improves the Robot? Toward Physical Recursive Self-Improvement ↗
Robots are increasingly autonomous in task execution, yet their improvement remains largely human-driven. This study investigates Physical Recursive Self-Improvement (Physical RSI), in which physical experience guides lasting updates to both a robot's capabilities and its improvement procedure, making subsequent improvement more effective across tasks. Physical RSI faces challenges from noisy observations, continuous dynamics, costly trials, and irreversible consequences. We present an L0-L5 framework that differentiates capability-level from meta-level updates and within-task from across-task…
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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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