Actor-Critic for Continuous Action Chunks: A Reinforcement Learning Framework for Long-Horizon Robotic Manipulation with Sparse Reward

Researchers introduced AC3 (Actor-Critic for Continuous Chunks), a new reinforcement learning framework that addresses challenges in long-horizon robotic manipulation tasks with sparse rewards. The system uses continuous action chunks with stabilization mechanisms and achieved superior performance on 25 benchmark tasks using minimal demonstrations.

• →AC3 framework enables stable learning of high-dimensional continuous action sequences for robotic manipulation.
• →The system uses asymmetric actor updates that learn exclusively from successful trajectories to ensure reliable policy improvement.
• →A self-supervised critic module provides intrinsic rewards at anchor points to handle sparse reward environments effectively.
• →Testing on 25 tasks from BiGym and RLBench benchmarks showed superior success rates with minimal demonstrations required.
• →The approach combines simple model architecture with targeted stabilization mechanisms for both actor and critic components.