Transfer Learning for Prosthetics Using Imitation Learning
On the map: Offline RL and imitation learning from data → Imitation learning methods
Abstract (arXiv)
In this paper, We Apply Reinforcement learning (RL) techniques to train a realistic biomechanical model to work with different people and on different walking environments. We benchmarking 3 RL algorithms: Deep Deterministic Policy Gradient (DDPG), Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) in OpenSim environment, Also we apply imitation learning to a prosthetics domain to reduce the training time needed to design customized prosthetics. We use DDPG algorithm to train an original expert agent. We then propose a modification to the Dataset Aggregation (DAgger) algorithm to reuse the expert knowledge and train a new target agent to replicate that behaviour in fewer than 5 iterations, compared to the 100 iterations taken by the expert agent which means reducing training time by 95%. Our modifications to the DAgger algorithm improve the balance between exploiting the expert policy and exploring the environment. We show empirically that these improve convergence time of the target agent, particularly when there is some degree of variation between expert and naive agent.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 3 of its 13 references that are in the map. First posted on arXiv 15 Jan 2019.
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3 of 13 references.
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- 2018Global overview of Imitation Learning19 Jan
Other references · 2
- 2015Continuous control with deep reinforcement learningcited by 3,279
- 2018Cross-Domain Transfer in Reinforcement Learning using Target Apprenticecited by 6
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- 2019Artificial Intelligence for Prosthetics - challenge solutions
- 2025An Introduction to Deep Reinforcement and Imitation Learning
- 2020Adversarial Imitation Learning via Random Search
- 2025Unlocking the Potential of Soft Actor-Critic for Imitation Learning
- 2026Actor-Critic Pretraining for Proximal Policy Optimization
- 2023RLIF: Interactive Imitation Learning as Reinforcement Learning
- 2026Simulation-Driven Imitation Learning for Biosignals-Free Shared-Autonomy Prosthetic Grasping
- 2018Accelerating Imitation Learning with Predictive Models