Keyframe-Guided Structured Rewards for Reinforcement Learning in Long-Horizon Laboratory Robotics

2026-02-28

AI Summary

This work addresses the challenges of low exploration efficiency and unstable convergence in long-horizon, high-precision laboratory robotic tasks, which arise from sparse rewards, multi-stage constraints, and noisy demonstrations. To overcome these issues, the authors propose a keyframe-guided structured reward generation framework that automatically extracts kinematics-aware keyframes from demonstrations. Leveraging a diffusion model in latent space, the method predicts stage-wise goals and constructs an explicit reward mechanism embedding task logic through geometric progress metrics. The system integrates multi-view visual encoding, a vision-language-action backbone, and human-in-the-loop reinforcement fine-tuning. Evaluated on four real-world biological experimentation tasks, the approach achieves an average success rate of 82% with only 40–60 minutes of online fine-tuning, substantially outperforming HG-DAgger (42%) and Hil-ConRFT (47%).

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

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

Abstract

Long-horizon precision manipulation in laboratory automation, such as pipette tip attachment and liquid transfer, requires policies that respect strict procedural logic while operating in continuous, high-dimensional state spaces. However, existing approaches struggle with reward sparsity, multi-stage structural constraints, and noisy or imperfect demonstrations, leading to inefficient exploration and unstable convergence. We propose a Keyframe-Guided Reward Generation Framework that automatically extracts kinematics-aware keyframes from demonstrations, generates stage-wise targets via a diffusion-based predictor in latent space, and constructs a geometric progress-based reward to guide online reinforcement learning. The framework integrates multi-view visual encoding, latent similarity-based progress tracking, and human-in-the-loop reinforcement fine-tuning on a Vision-Language-Action backbone to align policy optimization with the intrinsic stepwise logic of biological protocols. Across four real-world laboratory tasks, including high-precision pipette attachment and dynamic liquid transfer, our method achieves an average success rate of 82% after 40--60 minutes of online fine-tuning. Compared with HG-DAgger (42%) and Hil-ConRFT (47%), our approach demonstrates the effectiveness of structured keyframe-guided rewards in overcoming exploration bottlenecks and providing a scalable solution for high-precision, long-horizon robotic laboratory automation.

Problem

• long-horizon manipulation
• reward sparsity
• structured constraints
• laboratory robotics
• demonstration noise

Innovation

• Keyframe-Guided Rewards
• Diffusion-Based Predictor
• Latent Space Progress Tracking
• Vision-Language-Action Backbone
• Long-Horizon Robotic Manipulation