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Develop and evaluate a model-free reinforcement learning framework that leverages human demonstrations to solve complex robotic manipulation tasks under sparse reward conditions.

July 27, 2017Open Access

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

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Develop and evaluate a model-free reinforcement learning framework that leverages human demonstrations to solve complex robotic manipulation tasks under sparse reward conditions.
Augmented the Deep Deterministic Policy Gradient (DDPG) algorithm by feeding human kinesthetic force-controlled demonstrations into an experience replay buffer alongside autonomous exploratory transitions.
Implemented a prioritized replay mechanism to automatically optimize the sampling distribution between recorded demonstrations and novel trial-and-error interactions.
Evaluated performance across four simulated insertion environments and validated feasibility on a physical robotic system performing flexible clip insertion into a rigid fixture.
Demonstration-augmented DDPG successfully mastered all four simulated insertion tasks under sparse rewards, outperforming baseline DDPG which failed in the absence of engineered shaping rewards.
Real-world robotic validation achieved successful insertion of a flexible clip into a rigid target without requiring model-based controllers or task-specific reward shaping.

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Implication

Experimental study reveals improved policy learning in sparse-reward robotic insertion tasks, indicating that human demonstrations eliminate the need for reward engineering.

Cite This Study

Hester et al. (2017) studied this question.

synapsesocial.com/papers/6a7ca6d193373eeacd1909c9 https://doi.org/10.48550/arxiv.1707.08817

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Implication

Experimental study reveals improved policy learning in sparse-reward robotic insertion tasks, indicating that human demonstrations eliminate the need for reward engineering.

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

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Implication

Cite This Study

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

Key Points

Discussion

Implication

Cite This Study

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