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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
Plan-Bench: Can Existing VLMs Go Well Beyond Perception to Reason and Plan in Embodied Worlds? ↗
Vision-Language Models (VLMs) have achieved strong performance in visual perception and spatial reasoning, leading to growing interest in using them as high-level planners for embodied AI. However, physical tasks require models to integrate spatial information, reason over temporal states, and generate multi-step plans under various constraints, raising the question of whether current VLMs can reliably support such reasoning and planning independently of execution. We introduce Plan-Bench, a benchmark for evaluating the high-level reasoning and multi-step planning capabilities of VLMs relevant…
- arXiv · Published · First archived
RoboSimStudio: An Interactive Toolkit for Rapid Real2Sim of Robot Manipulation Tasks ↗
It is difficult to accurately model the physics of the interaction between a robot and the objects it manipulates, including contact mechanics, coupled contact dynamics, deformations, stiffness and friction. We present RoboSimStudio (RSS), an interactive toolkit that makes choices of solver, contact model, and physical parameters explicit and editable, allowing both humans and coding agents to iteratively refine simulations based on observed physical behavior. We implement RSS with the open Newton physics simulation system and describe experiments with human-guided real2sim using (1) visual ev…
- arXiv · Published · First archived
EgoMatrix: Physically Faithful Dexterous Robot Demonstrations from Egocentric Human Videos ↗
Egocentric human videos offer a scalable alternative to robot teleoperation for collecting dexterous manipulation data. However, two challenges limit their use as reliable training data: accurate hand-object interaction (HOI) reconstruction from monocular RGB and high-quality robot demonstrations, especially visual observations. We present EgoMatrix, an end-to-end data engine that converts monocular egocentric human videos into bimanual dexterous trajectories and paired robot observation videos, with the trajectories directly replayable on real robots. It reconstructs HOI with emphasis on rela…
- OpenAlex · Published · First archived
Joint optimization framework for robot dance motion generation, aesthetic evaluation, and dynamic balance control based on conditional gans and safe deep reinforcement learning ↗
Artificial intelligence and intelligent algorithms provide effective technical support for generating complex robotic dance motions and maintaining stable execution under dynamic constraints. To address the separation among motion synthesis, aesthetic evaluation, and balance control in existing robot dance systems, this study proposes a joint optimization framework that integrates a conditional Generative Adversarial Network (cGAN), multimodal aesthetic scoring, and safe Deep Reinforcement Learning (DRL). A temporal motion representation model is first constructed to encode joint angles, angul…
- Hacker News · Posted on Hacker News · First archived
Show HN: I turned a $4 toy robot into an AI companion with one web page ↗
Hacker News discussion.
- GitHub · Repository created · First archived
imaaiagent/fumi ↗
Open-source AI companion that lives on your desk. Built with ESP32-S3, touch, voice, and any AI model.
- arXiv · Published · First archived
Chronos Policy: Learning Robot Policies on Physical Time ↗
Robot policies often convert sensing and control into fixed-rate observation-action sequences, leaving physical time implicit in sequence position. This assumption breaks when observation rates or delays vary, or when inference latency changes. We introduce Chronos Policy, which makes physical time explicit by conditioning on observation capture times and desired action execution times. We study two ways to incorporate time into a diffusion transformer: adding timestamp embeddings to token features, or encoding physical time in positional encoding through time-aware rotary positional encodings…
- arXiv · Published · First archived
HorizonNav: Geometry-Guided Video Diffusion for Anticipatory 3D Robot Navigation ↗
We present HorizonNav, a video diffusion-based framework for anticipatory robot navigation that transforms a single RGB image and a text prompt into a geometrically aligned representation of plausible surrounding 3D space. Given the initial observation and a textual description of the reconstruction objective, a vision-language model, guided by designed instructions, interprets the scene and constructs structured prompts for a video diffusion model. The resulting video sequence provides plausible future observations of the environment, which are subsequently processed using monocular ORB-SLAM3…
- arXiv · Published · First archived
DreamTrue: Action-Faithful Robot World Model with Counterfactual Post-Training ↗
We present DreamTrue, a multi-view, cross-embodiment robot world model for action-faithful and physically plausible video prediction. Training such a model on existing robot datasets faces two obstacles: imprecise calibration can impair action following, while limited coverage of unsuccessful interactions can bias predictions toward successful outcomes. To improve action following across embodiments, we render action trajectories into image-space conditions and introduce offline geometric calibration to align these conditions with the target videos. To broaden interaction coverage, we introduc…
- arXiv · Published · First archived
A Balanced Data Diet: Addressing the Exploration Bottleneck in Mega-Scale RL for Robot Control ↗
General-purpose robots must perform a wide range of tasks from agile locomotion to dexterous manipulation. While sim-to-real reinforcement learning (RL) has proven to be a useful tool for this goal, current RL pipelines depend on engineering-heavy, per-task structural priors such as shaped rewards and demonstrations. Recent work has shown that diverse simulator resets, combined with massively parallel simulation, can alleviate much of this engineering burden on several manipulation problems. However, we find that naively scaling this paradigm to more precise or dynamic problems remains non-tri…
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How to assess evidence about AI and robotics
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