# Dataset development

Published articles for Dataset development.

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## Amazon and University of Michigan give robots a sense of touch

DevFeed: [Amazon and University of Michigan give robots a sense of touch](<https://devfeed.tech/articles/amazon-and-university-of-michigan-give-robots-a-sense-of-touch-7593.md>)

Original publisher: [Read original article](<https://www.amazon.science/blog/amazon-and-university-of-michigan-give-robots-a-sense-of-touch>)

Author: Mani Nambi; Nima Fazeli

Published: 2026-07-10T17:13:31Z

Content type: article

Language: en

Sources: [Amazon Science homepage](<https://devfeed.tech/sources/amazon-science-homepage.md>)

Topics: [contact-rich manipulation](<https://devfeed.tech/topics/contact-rich-manipulation.md>)

Tags: [amazon](<https://devfeed.tech/tags/amazon.md>), [amazon-robotics-research](<https://devfeed.tech/tags/amazon-robotics-research.md>), [contact-rich-manipulation](<https://devfeed.tech/tags/contact-rich-manipulation.md>), [dataset-development](<https://devfeed.tech/tags/dataset-development.md>), [dexterous-robot-manipulation](<https://devfeed.tech/tags/dexterous-robot-manipulation.md>), [gelsight-mini-sensor](<https://devfeed.tech/tags/gelsight-mini-sensor.md>), [hydroelastic-contact-model](<https://devfeed.tech/tags/hydroelastic-contact-model.md>), [hydroshear-simulator](<https://devfeed.tech/tags/hydroshear-simulator.md>), [physics](<https://devfeed.tech/tags/physics.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [reinforcement-learning-robotics](<https://devfeed.tech/tags/reinforcement-learning-robotics.md>), [robot-grasping-simulation](<https://devfeed.tech/tags/robot-grasping-simulation.md>), [robot-sense-of-touch](<https://devfeed.tech/tags/robot-sense-of-touch.md>), [robotic-manipulation](<https://devfeed.tech/tags/robotic-manipulation.md>), [robotics](<https://devfeed.tech/tags/robotics.md>), [robots](<https://devfeed.tech/tags/robots.md>), [sim-to-real-transfer-robotics](<https://devfeed.tech/tags/sim-to-real-transfer-robotics.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

HydroShear is a physics-based tactile-force simulator that trains robots for dexterous, contact-rich manipulation in simulation and transfers the resulting policies to real-world tasks.

### Source excerpt

HydroShear, a new physics-based simulator, teaches robots how to use their sense of touch to perform complex manipulation tasks, in a way that transfers seamlessly to the real world.

## Capturing token IDs during agentic interactions for better reinforcement learning

DevFeed: [Capturing token IDs during agentic interactions for better reinforcement learning](<https://devfeed.tech/articles/capturing-token-ids-during-agentic-interactions-for-better-reinforcement-learning-7595.md>)

Original publisher: [Read original article](<https://www.amazon.science/blog/capturing-token-ids-during-agentic-interactions-for-better-reinforcement-learning>)

Author: Frederick Robinson

Published: 2026-07-09T12:46:00Z

Content type: article

Language: en

Sources: [Amazon Science homepage](<https://devfeed.tech/sources/amazon-science-homepage.md>)

Topics: [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Agent Harness](<https://devfeed.tech/topics/agent-harness.md>), [tokenization](<https://devfeed.tech/topics/tokenization.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [Rust](<https://devfeed.tech/topics/rust.md>), [computer-use](<https://devfeed.tech/topics/computer-use.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [amazon-agi-lab](<https://devfeed.tech/tags/amazon-agi-lab.md>), [computer-use](<https://devfeed.tech/tags/computer-use.md>), [dataset-development](<https://devfeed.tech/tags/dataset-development.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [model](<https://devfeed.tech/tags/model.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [rust](<https://devfeed.tech/tags/rust.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

This article explains how Turnstile, a Rust proxy placed between an agent harness and a model backend, captures exact token IDs during generation. The recorded token-level trajectories preserve information that text transcripts can lose and can be passed into reinforcement-learning training stacks. Reported validations cover a text-only coding agent and a multimodal computer-use agent whose performance improved during RL runs.

### Source excerpt

A new Rust proxy called Turnstile sits between the model backend and the agent harness to capture information lost in mere text transcripts.