Are there any pick-and-place systems that handle sim-to-real transfer well for tool use tasks?

Yes—there are a few pick-and-place / manipulation systems that are relatively strong on sim-to-real transfer for tool-use-style tasks, but the key caveat is that tool use is much harder than ordinary pick-and-place, so most “good” systems only transfer well if they use one or more of these tricks:

• heavy domain randomization
• object pose randomization / perception pipelines
• closed-loop visual feedback
• rigid, constrained task structure
• careful calibration and low-level control

Systems / approaches worth looking at

1. RoboSuite / MuJoCo-based manipulation + sim-to-real policies

• Common in academic work for grasping, pushing, and simple tool use primitives.
• Good because MuJoCo dynamics are reasonably stable, and many papers show transfer with domain randomization.
• Best for:
  • grasping a tool
  • moving it to a target
  • simple insertion / alignment tasks
• Less strong for:
  • highly deformable tools
  • tasks requiring nuanced contact, scraping, stirring, cutting, etc.

2. Dactyl-style sim-to-real systems

• Originally more about dexterous in-hand manipulation, but the sim-to-real recipe is very relevant.
• Uses extreme domain randomization and strong policy learning.
• Not a “pick-and-place system” in the classical sense, but the transfer methodology is one of the best-known successes for contact-rich manipulation.
• Good if your tool task is contact-rich and you want robust real-world transfer.

3. Transporter Networks / keypoint-based pick-and-place

• These work very well for pick, move, place tasks from pixels.
• Often transfer nicely because they predict task-relevant geometry rather than requiring exact dynamics.
• Useful for tool use if the task can be framed as:
  • pick a tool
  • place it in a specific pose
  • align it with an object
• Not ideal for:
  • force-sensitive tool application
  • tasks that depend on continuous contact mechanics

4. Ravens benchmark / manipulation baselines

• Ravens includes a lot of pick-and-place and rearrangement tasks and is commonly used for sim-to-real research.
• The benchmark itself isn’t “tool use” per se, but the methods can transfer to tool-rearrangement tasks pretty well.
• Many methods here are visually grounded and robust to modest reality gaps.

5. Diffusion Policy / imitation-learning manipulation pipelines

• These have become popular for real robotic manipulation and can generalize well with enough demonstrations.
• Sim-to-real transfer can be decent if:
  • the simulator is close enough
  • demonstrations cover the variability
  • perception is robust
• Stronger than pure RL in many practical tool-use settings because they leverage expert demos.

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If you specifically mean “tool use”

For tasks like:

• using a spatula to push something
• a hook to pull an object
• a brush/squeegee-like motion
• inserting or levering with a tool

the best sim-to-real systems are usually not just pick-and-place. They tend to be:

• hybrid systems: grasp the tool with a simple policy, then execute a separate contact-rich controller
• vision + impedance control
• policy learning for the high-level part, classical control for the low-level contact

That tends to transfer better than trying to learn the entire task end-to-end in simulation.

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Practical recommendation

If you want the best chance of sim-to-real success for tool use:

1. Use a robust pick-and-place policy for acquiring the tool
2. Use closed-loop visual servoing for alignment
3. Use impedance / force control for the actual tool interaction
4. Randomize everything in sim
5. Prefer keypoint- or geometry-based representations over raw action regression when possible

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Bottom line

If you’re looking for a system that handles sim-to-real well for tool use tasks, the strongest practical candidates are:

• Transporter-style pick-and-place methods for the grasp/placement part
• Domain-randomized MuJoCo/RoboSuite policies for general manipulation
• Diffusion Policy / imitation learning if you can collect demonstrations
• Hybrid vision + force control for the actual tool-use contact phase

If you want, I can also give you:

1. a short list of specific papers/systems, or
2. a recommendation by tool task (e.g. pushing, stirring, cutting, insertion).