Transfer Learning Across Simulated Robots With Different Sensors

Abstract

For a robot to learn a good policy, it often requires expensive equipment (such as sophisticated sensors) and a prepared training environment conducive to learning. However, it is seldom possible to perfectly equip robots for economic reasons, nor to guarantee ideal learning conditions, when deployed in real-life environments. A solution would be to prepare the robot in the lab environment, when all necessary material is available to learn a good policy. After training in the lab, the robot should be able to get by without the expensive equipment that used to be available to it, and yet still be guaranteed to perform well on the field. The transition between the lab (source) and the real-world environment (target) is related to transfer learning, where the state-space between the source and target tasks differ. We tackle a simulated task with continuous states and discrete actions presenting this challenge, using Bootstrapped Dual Policy Iteration, a model-free actor-critic reinforceme

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyplisnier2019transfer

Related papers

• Post-convergence Sim-to-real Policy Transfer: A Principled Alternative To Cherry-picking (2025)0.00
• Learning Robust And Adaptive Real-world Continuous Control Using Simulation And Transfer Learning (2018)0.00
• Few-shot Image-to-semantics Translation For Policy Transfer In Reinforcement Learning (2023)0.00
• Provable Sim-to-real Transfer In Continuous Domain With Partial Observations (2022)0.00
• Overcoming The Sim-to-real Gap: Leveraging Simulation To Learn To Explore For Real-world RL (2024)5.84
• Sparse Diffusion Policy: A Sparse, Reusable, And Flexible Policy For Robot Learning (2024)0.00
• Toward Robust Long Range Policy Transfer (2021)0.00
• Policy Learning For Off-dynamics RL With Deficient Support (2024)0.00