AI in Robotics
SigEnt-SAC: Advancing Real-World Robot Learning with One-Shot Efficiency
This paper introduces SigEnt-SAC, an off-policy actor-critic method designed for real-world robot learning. It achieves rapid convergence and high performance even with extremely limited data, such as a single expert demonstration, by leveraging a novel sigmoid-bounded entropy formulation and gated behavior cloning. The method demonstrates robust learning from scratch across diverse robotic embodiments, visual observations, and dynamic environments.
Executive Impact Summary
SigEnt-SAC significantly reduces data acquisition costs and accelerates deployment in complex real-world robotic scenarios.
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Success Rate
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Reduction in Task Completion Time
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Single Expert Trajectory Required
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Methodology
Performance Benchmarks
Sigmoid-Bounded Entropy
Prevents negative-entropy-driven optimization
Enterprise Process Flow
Initialize with Replay Buffers
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Collect Expert Transitions
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Online Interaction & Training Loop
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Critic Update (Sigmoid-Bounded Entropy)
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Policy Update (Gated Behavior Cloning)
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Target Network Update
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100%
Success Rate in D4RL Tasks (One-Shot)
Real-World Robotic Tasks
Client: Robotics Research Lab
Challenge: Develop a low-cost RL method for real-world robots with minimal data, robust to noisy visual observations and dynamic environments.
Solution: Implemented SigEnt-SAC on four real-world robotic tasks (Push-Cube, Ball-Driving, Slalom Quadruped, Slalom Humanoid) learning from raw images and sparse rewards.
Result: SigEnt-SAC achieved 100% success rates across all tasks with only a single demonstration, learning faster and more stable policies than traditional methods. Achieved an average 40.9% reduction in task completion time.
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