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SimEX Framework and Walden Robotics Strategy Announced

2 reports1 reporting sourcesUpdated 3 days ago

Overview

Event synthesis

Researchers Jiaheng Hu and Steve Tod introduced SimEX, a simulation-based framework for training robots on real-world manipulation tasks. Concurrently, Walden robotics outlined its full-stack approach emphasizing robust simulations and teleoperation data collection.

Generated from attributed reports · Updated 12 hours ago

Event evidence and corrections

1 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.

Environment: simulation
Supporting report

“Using a simulation that does not match the world very well, you can let an agent build and acquire skills that it can then use on a real ...”

Exact source · revision 1

chris-paxton

Latest development2026-11-10 21:51 UTC
Walden robotics: be full stack, build a good simulation, build a good teleop stack and collect human intervention data at scale

Developments

2 developments
  1. 2026-11-10 21:51 UTC · 1 reports
    Walden Robotics full-stack development strategy
    Chris Paxton — X Robotics：Walden robotics: be full stack, build a good simulation, build a good teleop stack and collect human intervention data at scale
  2. 2026-11-10 15:18 UTC · 1 reports
    SimEX: Simulation-based auto-research framework for robotics skills
    Chris Paxton — X Robotics：SimEX: A simulation-based auto-research framework for developing robot skills transferable to real-world tasks

Report timeline

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11/10
  1. Chris Paxton — X Robotics
    Walden robotics: be full stack, build a good simulation, build a good teleop stack and collect human intervention data at scale

    Walden robotics: be full stack, build a good simulation, build a good teleop stack and collect human intervention data at scale.

  2. Chris Paxton — X RoboticsSignal
    SimEX: A simulation-based auto-research framework for developing robot skills transferable to real-world tasks

    This article relays a research project called SimEX, which uses imperfect simulations to train agents that can perform manipulation tasks on real robots. The framework allows agents to build a robust toolbox of skills applicable to complex goals like folding towels and manipulating plates. The content is attributed to researchers Jiaheng Hu and Steve Tod, shared via the RoboPapers podcast series.

Event coverage history

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Timezone · UTC

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