# Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor Control

arXiv: https://arxiv.org/abs/1910.07972 · HTML: https://mlsanity.com/paper/1910.07972/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes](https://mlsanity.com/paper/2110.06192/) (2021)
- [Robot Skill Adaptation via Soft Actor-Critic Gaussian Mixture Models](https://mlsanity.com/paper/2111.13129/) (2021)
- [Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty](https://mlsanity.com/paper/2106.04306/) (2021)
- [Pre-training of Deep RL Agents for Improved Learning under Domain Randomization](https://mlsanity.com/paper/2104.14386/) (2021)
- [Case-Based Inverse Reinforcement Learning Using Temporal Coherence](https://mlsanity.com/paper/2206.05827/) (2022)
- [Tactile Estimation of Extrinsic Contact Patch for Stable Placement](https://mlsanity.com/paper/2309.14552/) (2023)
- [Learning from Demonstrations via Capability-Aware Goal Sampling](https://mlsanity.com/paper/2601.08731/) (2026)
- [DexMimic: A Generalist Dexterous Hand-Object Tracking Policy via Value-Guided Reset Allocation](https://mlsanity.com/paper/2610.19089/) (2026)

## Abstract (arXiv)

We propose Adaptive Curriculum Generation from Demonstrations (ACGD) for reinforcement learning in the presence of sparse rewards. Rather than designing shaped reward functions, ACGD adaptively sets the appropriate task difficulty for the learner by controlling where to sample from the demonstration trajectories and which set of simulation parameters to use. We show that training vision-based control policies in simulation while gradually increasing the difficulty of the task via ACGD improves the policy transfer to the real world. The degree of domain randomization is also gradually increased through the task difficulty. We demonstrate zero-shot transfer for two real-world manipulation tasks: pick-and-stow and block stacking. A video showing the results can be found at https://lmb.informatik.uni-freiburg.de/projects/curriculum/