Next-Future: Sample-Efficient Policy Learning for Robotic-Arm Tasks

2025-04-15

AI Summary

In multi-objective robotic manipulation tasks, sparse rewards severely hinder sample efficiency. Classical Hindsight Experience Replay (HER) relies on heuristic goal relabeling, lacking theoretical grounding and failing to meet high-precision control requirements. To address this, we propose Next-Future Policy—a principled replay mechanism grounded in one-step transition rewards. It incorporates goal-aware one-step reward reweighting, an improved Q-function update rule, and multi-goal value approximation to achieve more accurate value estimation. Unlike HER’s heuristic design, Next-Future Policy provides a theoretically motivated alternative for goal-conditioned reinforcement learning. Evaluated across eight simulated robotic manipulation tasks, it improves sample efficiency in seven and success rate in six. Furthermore, its effectiveness and robustness are validated on a real robotic arm. The method advances the state of the art by bridging the gap between theoretical rigor and practical performance in sparse-reward, multi-goal settings.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Hindsight Experience Replay (HER) is widely regarded as the state-of-the-art algorithm for achieving sample-efficient multi-goal reinforcement learning (RL) in robotic manipulation tasks with binary rewards. HER facilitates learning from failed attempts by replaying trajectories with redefined goals. However, it relies on a heuristic-based replay method that lacks a principled framework. To address this limitation, we introduce a novel replay strategy,"Next-Future", which focuses on rewarding single-step transitions. This approach significantly enhances sample efficiency and accuracy in learning multi-goal Markov decision processes (MDPs), particularly under stringent accuracy requirements -- a critical aspect for performing complex and precise robotic-arm tasks. We demonstrate the efficacy of our method by highlighting how single-step learning enables improved value approximation within the multi-goal RL framework. The performance of the proposed replay strategy is evaluated across eight challenging robotic manipulation tasks, using ten random seeds for training. Our results indicate substantial improvements in sample efficiency for seven out of eight tasks and higher success rates in six tasks. Furthermore, real-world experiments validate the practical feasibility of the learned policies, demonstrating the potential of"Next-Future"in solving complex robotic-arm tasks.

Problem

• Improving sample efficiency in robotic-arm multi-goal RL
• Enhancing accuracy in learning multi-goal MDPs
• Developing a principled replay strategy for robotic manipulation

Innovation

• Introduces Next-Future replay strategy
• Focuses on single-step transition rewards
• Enhances sample efficiency in multi-goal RL