# Hardware Conditioned Policies for Multi-Robot Transfer Learning

arXiv: https://arxiv.org/abs/1811.09864 · HTML: https://mlsanity.com/paper/1811.09864/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2018.

## Built on by (most cited first, 30 of 46)

- [A Generalist Agent](https://mlsanity.com/paper/2205.06175/) (2022)
- [Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation](https://mlsanity.com/paper/2408.11812/) (2024)
- [MetaMorph: Learning Universal Controllers with Transformers](https://mlsanity.com/paper/2203.11931/) (2022)
- [Can Wikipedia Help Offline Reinforcement Learning?](https://mlsanity.com/paper/2201.12122/) (2022)
- [FLOWER: Democratizing Generalist Robot Policies with Efficient Vision-Language-Action Flow Policies](https://mlsanity.com/paper/2509.04996/) (2025)
- [GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots](https://mlsanity.com/paper/2209.05309/) (2022)
- [Polybot: Training One Policy Across Robots While Embracing Variability](https://mlsanity.com/paper/2307.03719/) (2023)
- [Block Contextual MDPs for Continual Learning](https://mlsanity.com/paper/2110.06972/) (2021)
- [Universal Morphology Control via Contextual Modulation](https://mlsanity.com/paper/2302.11070/) (2023)
- [Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms](https://mlsanity.com/paper/2103.03697/) (2021)
- [Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning](https://mlsanity.com/paper/2203.13733/) (2022)
- [Dynamics Generalisation in Reinforcement Learning via Adaptive Context-Aware Policies](https://mlsanity.com/paper/2310.16686/) (2023)
- [HyperDynamics: Meta-Learning Object and Agent Dynamics with Hypernetworks](https://mlsanity.com/paper/2103.09439/) (2021)
- [Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges](https://mlsanity.com/paper/2311.18044/) (2023)
- [The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Models](https://mlsanity.com/paper/2402.01874/) (2024)
- [REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer](https://mlsanity.com/paper/2202.05244/) (2022)
- [A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation](https://mlsanity.com/paper/2211.14296/) (2022)
- [Subequivariant Graph Reinforcement Learning in 3D Environments](https://mlsanity.com/paper/2305.18951/) (2023)
- [OXE-AugE: A Large-Scale Robot Augmentation of OXE for Scaling Cross-Embodiment Policy Learning](https://mlsanity.com/paper/2512.13100/) (2025)
- [Tool-as-Interface: Learning Robot Policies from Observing Human Tool Use](https://mlsanity.com/paper/2504.04612/) (2025)
- [AnyBody: A Benchmark Suite for Cross-Embodiment Manipulation](https://mlsanity.com/paper/2505.14986/) (2025)
- [Residual Model Learning for Microrobot Control](https://mlsanity.com/paper/2104.00631/) (2021)
- [RMBench: Benchmarking Deep Reinforcement Learning for Robotic Manipulator Control](https://mlsanity.com/paper/2210.11262/) (2022)
- [COMPOSER: Scalable and Robust Modular Policies for Snake Robots](https://mlsanity.com/paper/2310.00871/) (2023)
- [Dynamics-Aligned Latent Imagination in Contextual World Models for Zero-Shot Generalization](https://mlsanity.com/paper/2508.20294/) (2025)
- [Learning Decentralized Multi-Biped Control for Payload Transport](https://mlsanity.com/paper/2406.17279/) (2024)
- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [Invariance Co-training for Robot Visual Generalization](https://mlsanity.com/paper/2512.05230/) (2025)
- [Morphology-Conditioned World Model for Cross-Embodiment Quadrupedal Locomotion](https://mlsanity.com/paper/2604.08780/) (2026)
- [Subequivariant Reinforcement Learning in 3D Multi-Entity Physical Environments](https://mlsanity.com/paper/2407.12505/) (2024)

## Abstract (arXiv)

Deep reinforcement learning could be used to learn dexterous robotic policies but it is challenging to transfer them to new robots with vastly different hardware properties. It is also prohibitively expensive to learn a new policy from scratch for each robot hardware due to the high sample complexity of modern state-of-the-art algorithms. We propose a novel approach called textitHardware Conditioned Policies where we train a universal policy conditioned on a vector representation of robot hardware. We considered robots in simulation with varied dynamics, kinematic structure, kinematic lengths and degrees-of-freedom. First, we use the kinematic structure directly as the hardware encoding and show great zero-shot transfer to completely novel robots not seen during training. For robots with lower zero-shot success rate, we also demonstrate that fine-tuning the policy network is significantly more sample-efficient than training a model from scratch. In tasks where knowing the agent dynamics is important for success, we learn an embedding for robot hardware and show that policies conditioned on the encoding of hardware tend to generalize and transfer well. The code and videos are available on the project webpage: https://sites.google.com/view/robot-transfer-hcp.