# Humanoid Rickshaw Pulling: Whole-Body Locomotion under Coupled Wheeled Loads

arXiv: https://arxiv.org/abs/2610.04238 · HTML: https://mlsanity.com/paper/2610.04238/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 29 references that are in the map.
- First posted on arXiv 3 Oct 2026.

## Builds on (most cited first, 25 of 25)

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- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (2021)
- [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)
- [Sim-to-Real: Learning Agile Locomotion For Quadruped Robots](https://mlsanity.com/paper/1804.10332/) (2018)
- [Asymmetric Actor Critic for Image-Based Robot Learning](https://mlsanity.com/paper/1710.06542/) (2017)
- [Real-World Humanoid Locomotion with Reinforcement Learning](https://mlsanity.com/paper/2303.03381/) (2023)
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- [Adapting Rapid Motor Adaptation for Bipedal Robots](https://mlsanity.com/paper/2205.15299/) (2022)
- [FALCON: Learning Force-Adaptive Humanoid Loco-Manipulation](https://mlsanity.com/paper/2505.06776/) (2025)
- [A Unified MPC Framework for Whole-Body Dynamic Locomotion and Manipulation](https://mlsanity.com/paper/2103.00946/) (2021)
- [Sim-to-Real Learning for Humanoid Box Loco-Manipulation](https://mlsanity.com/paper/2310.03191/) (2023)
- [mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning](https://mlsanity.com/paper/2601.22074/) (2026)
- [FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots](https://mlsanity.com/paper/2505.06883/) (2025)
- [Learning Human-Humanoid Coordination for Collaborative Object Carrying](https://mlsanity.com/paper/2510.14293/) (2025)
- [HMC: Learning Heterogeneous Meta-Control for Contact-Rich Loco-Manipulation](https://mlsanity.com/paper/2511.14756/) (2025)
- [HAIC: Humanoid Agile Object Interaction Control via Dynamics-Aware World Model](https://mlsanity.com/paper/2602.11758/) (2026)
- [HUSKY: Humanoid Skateboarding System via Physics-Aware Whole-Body Control](https://mlsanity.com/paper/2602.03205/) (2026)
- [Efficient and Versatile Quadrupedal Skating: Optimal Co-design via Reinforcement Learning and Bayesian Optimization](https://mlsanity.com/paper/2603.18408/) (2026)
- [VAIC: Vision-Guided Humanoid Agile Object Interaction Control via Decoupled Commands](https://mlsanity.com/paper/2606.09286/) (2026)
- [On Centroidal Dynamics and Integrability of Average Angular Velocity](https://mlsanity.com/paper/1701.02514/) (2017)

## Abstract (arXiv)

Humanoid robots could transport payloads substantially heavier than themselves by pulling passive wheeled vehicles instead of carrying the load. This capability, however, creates a coupled locomotion problem: the robot must maintain persistent upper-body contact while adapting to unknown, configuration-dependent forces arising from the payload, vehicle, and terrain. We present a whole-body control framework for humanoid rickshaw pulling that tracks commanded vehicle motion while preserving balance and stable grasps under uncertain load dynamics. During training, a privileged teacher exploits vehicle states, interaction forces, and load properties. Its actions and latent are distilled into a history-conditioned student that implicitly infers coupled dynamics from proprioceptive responses, followed by reinforcement-learning fine-tuning. Comparisons with No History and Only History baselines show that the resulting policy achieves accurate vehicle tracking while reducing vehicle oscillation, torso tilt, and actuation cost. Behavioral analysis shows that Unitree G1 propels the rickshaw and generates gait-synchronized whole-body reactions that stabilize its lateral and roll motions. Moreover, pulling redistributes joint effort and yields a lower robot-normalized cost-of-transport proxy than unloaded walking over most tested load--speed conditions. On hardware, a single policy performs starting, sustained pulling, turning, and stopping with both rigid payloads and human passengers, handling a loaded rickshaw mass of up to 115~kg without load-specific retuning. These results demonstrate robust heavy-load transportation through coordinated and persistent humanoid--vehicle interaction.