# Olaf: Bringing an Animated Character to Life in the Physical World

arXiv: https://arxiv.org/abs/2512.16705 · HTML: https://mlsanity.com/paper/2512.16705/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 44 references that are in the map.
- First posted on arXiv 18 Dec 2025.

## Builds on (most cited first, 20 of 20)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Learning Agile Robotic Locomotion Skills by Imitating Animals](https://mlsanity.com/paper/2004.00784/) (2020)
- [DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills](https://mlsanity.com/paper/1804.02717/) (2018)
- [ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots](https://mlsanity.com/paper/2306.14874/) (2023)
- [ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills](https://mlsanity.com/paper/2502.01143/) (2025)
- [Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots](https://mlsanity.com/paper/2103.14295/) (2021)
- [Feedback Control For Cassie With Deep Reinforcement Learning](https://mlsanity.com/paper/1803.05580/) (2018)
- [Attention-Based Map Encoding for Learning Generalized Legged Locomotion](https://mlsanity.com/paper/2506.09588/) (2025)
- [Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning](https://mlsanity.com/paper/2505.11164/) (2025)
- [Berkeley Humanoid: A Research Platform for Learning-based Control](https://mlsanity.com/paper/2407.21781/) (2024)
- [Contact-Aided Invariant Extended Kalman Filtering for Robot State Estimation](https://mlsanity.com/paper/1904.09251/) (2019)
- [Design and Control of a Bipedal Robotic Character](https://mlsanity.com/paper/2501.05204/) (2025)
- [Towards bridging the gap: Systematic sim-to-real transfer for diverse legged robots](https://mlsanity.com/paper/2509.06342/) (2025)
- [Dynamic Walking with Compliance on a Cassie Bipedal Robot](https://mlsanity.com/paper/1904.11104/) (2019)
- [Learning Quiet Walking for a Small Home Robot](https://mlsanity.com/paper/2502.10983/) (2025)
- [QuietPaw: Learning Quadrupedal Locomotion with Versatile Noise Preference Alignment](https://mlsanity.com/paper/2503.05035/) (2025)
- [From Screen to Stage: Kid Cosmo, A Life-Like, Torque-Controlled Humanoid for Entertainment Robotics](https://mlsanity.com/paper/2508.11884/) (2025)

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

- [SteadyTray: Learning Object Balancing Tasks in Humanoid Tray Transport via Residual Reinforcement Learning](https://mlsanity.com/paper/2603.10306/) (2026)
- [MPC-Injection: Biasing Off-Policy Locomotion RL Toward Controller-Induced Behavior Basins](https://mlsanity.com/paper/2606.26392/) (2026)
- [Fauna Sprout: A lightweight, approachable, developer-ready humanoid robot](https://mlsanity.com/paper/2601.18963/) (2026)
- [IRIS: Learning-Driven Task-Specific Cinema Robot Arm for Visuomotor Motion Control](https://mlsanity.com/paper/2602.17537/) (2026)
- [QuietWalk: Physics-Informed Reinforcement Learning for Ground Reaction Force-Aware Humanoid Locomotion Under Diverse Footwear](https://mlsanity.com/paper/2604.23702/) (2026)
- [Robot Learning to Communicate through Projected Visual Abstractions](https://mlsanity.com/paper/2607.22434/) (2026)
- [STyMo: Fast and Controllable Few-Shot Motion Style Transfer](https://mlsanity.com/paper/2609.04500/) (2026)
- [Beyond Tasks: A Vision for Reproducing an Animal-like Behavioral Substrate Using Modern Robot Learning Techniques](https://mlsanity.com/paper/2609.33165/) (2026)

## Abstract (arXiv)

Animated characters often move in non-physical ways and have proportions that are far from a typical walking robot. This provides an ideal platform for innovation in both mechanical design and stylized motion control. In this paper, we bring Olaf to life in the physical world, relying on reinforcement learning guided by animation references for control. To create the illusion of Olaf's feet moving along his body, we hide two asymmetric legs under a soft foam skirt. To fit actuators inside the character, we use spherical and planar linkages in the arms, mouth, and eyes. Because the walk cycle results in harsh contact sounds, we introduce additional rewards that noticeably reduce impact noise. The large head, driven by small actuators in the character's slim neck, creates a risk of overheating, amplified by the costume. To keep actuators from overheating, we feed temperature values as additional inputs to policies, introducing new rewards to keep them within bounds. We validate the efficacy of our modeling in simulation and on hardware, demonstrating an unmatched level of believability for a costumed robotic character.