# Learning Decentralized Multi-Biped Control for Payload Transport

arXiv: https://arxiv.org/abs/2406.17279 · HTML: https://mlsanity.com/paper/2406.17279/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 30 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 21 of 21)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Legged Locomotion in Challenging Terrains using Egocentric Vision](https://mlsanity.com/paper/2211.07638/) (2022)
- [Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior](https://mlsanity.com/paper/2212.03238/) (2022)
- [Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning](https://mlsanity.com/paper/2304.13653/) (2023)
- [3D Neural Scene Representations for Visuomotor Control](https://mlsanity.com/paper/2107.04004/) (2021)
- [Blind Bipedal Stair Traversal via Sim-to-Real Reinforcement Learning](https://mlsanity.com/paper/2105.08328/) (2021)
- [SORNet: Spatial Object-Centric Representations for Sequential Manipulation](https://mlsanity.com/paper/2109.03891/) (2021)
- [Hardware Conditioned Policies for Multi-Robot Transfer Learning](https://mlsanity.com/paper/1811.09864/) (2018)
- [Learning Vision-Based Bipedal Locomotion for Challenging Terrain](https://mlsanity.com/paper/2309.14594/) (2023)
- [Learning Memory-Based Control for Human-Scale Bipedal Locomotion](https://mlsanity.com/paper/2006.02402/) (2020)
- [Revisiting Reward Design and Evaluation for Robust Humanoid Standing and Walking](https://mlsanity.com/paper/2404.19173/) (2024)
- [Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning](https://mlsanity.com/paper/2405.02425/) (2024)
- [Sim-to-Real Learning for Bipedal Locomotion Under Unsensed Dynamic Loads](https://mlsanity.com/paper/2204.04340/) (2022)
- [Safety-Critical Coordination for Cooperative Legged Locomotion via Control Barrier Functions](https://mlsanity.com/paper/2303.13630/) (2023)
- [End-to-end Decentralized Multi-robot Navigation in Unknown Complex Environments via Deep Reinforcement Learning](https://mlsanity.com/paper/1907.01713/) (2019)
- [Layered Control for Cooperative Locomotion of Two Quadrupedal Robots: Centralized and Distributed Approaches](https://mlsanity.com/paper/2211.06913/) (2022)
- [Distributed Data-Driven Predictive Control for Multi-Agent Collaborative Legged Locomotion](https://mlsanity.com/paper/2211.06917/) (2022)
- [Collaborative Planning for Catching and Transporting Objects in Unstructured Environments](https://mlsanity.com/paper/2311.07100/) (2023)
- [Energy Conscious Over-actuated Multi-Agent Payload Transport Robot: Simulations and Preliminary Physical Validation](https://mlsanity.com/paper/1909.04655/) (2019)

## Built on by (most cited first, 4 of 4)

- [Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing](https://mlsanity.com/paper/2411.07104/) (2024)
- [Internal State-Based Policy Gradient Methods for Partially Observable Markov Potential Games](https://mlsanity.com/paper/2604.00433/) (2026)
- [Learning Multi-Humanoid Pickup and Transport via Decentralized Object-Centric Control](https://mlsanity.com/paper/2609.17824/) (2026)
- [Watch, Infer, Coordinate: Inferring Robot Partner Constraints for Zero-Shot Coordination](https://mlsanity.com/paper/2610.02170/) (2026)

## Abstract (arXiv)

Payload transport over flat terrain via multi-wheel robot carriers is well-understood, highly effective, and configurable. In this paper, our goal is to provide similar effectiveness and configurability for transport over rough terrain that is more suitable for legs rather than wheels. For this purpose, we consider multi-biped robot carriers, where wheels are replaced by multiple bipedal robots attached to the carrier. Our main contribution is to design a decentralized controller for such systems that can be effectively applied to varying numbers and configurations of rigidly attached bipedal robots without retraining. We present a reinforcement learning approach for training the controller in simulation that supports transfer to the real world. Our experiments in simulation provide quantitative metrics showing the effectiveness of the approach over a wide variety of simulated transport scenarios. In addition, we demonstrate the controller in the real-world for systems composed of two and three Cassie robots. To our knowledge, this is the first example of a scalable multi-biped payload transport system.