Hyper-GoalNet: Goal-Conditioned Manipulation Policy Learning with HyperNetworks

2025-11-26

AI Summary

To address the poor generalization and unstable performance of goal-conditioned policies across diverse environments and tasks, this paper proposes a hypernetwork-based goal-conditioned policy learning framework. Methodologically: (1) goal information dynamically generates policy network parameters, decoupling goal encoding from state processing; (2) forward dynamics are jointly modeled in a latent space with an explicit distance-based constraint to ensure monotonic convergence toward the goal state. Technically, the approach integrates hypernetwork architecture, goal-conditioned policy representation, forward dynamics modeling, and distance-driven latent-space regularization. Experiments demonstrate that our method significantly outperforms existing baselines across multiple robotic manipulation tasks—particularly under high environmental stochasticity. Real-robot evaluations further confirm its strong robustness to sensor noise and physical uncertainties.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Goal-conditioned policy learning for robotic manipulation presents significant challenges in maintaining performance across diverse objectives and environments. We introduce Hyper-GoalNet, a framework that generates task-specific policy network parameters from goal specifications using hypernetworks. Unlike conventional methods that simply condition fixed networks on goal-state pairs, our approach separates goal interpretation from state processing -- the former determines network parameters while the latter applies these parameters to current observations. To enhance representation quality for effective policy generation, we implement two complementary constraints on the latent space: (1) a forward dynamics model that promotes state transition predictability, and (2) a distance-based constraint ensuring monotonic progression toward goal states. We evaluate our method on a comprehensive suite of manipulation tasks with varying environmental randomization. Results demonstrate significant performance improvements over state-of-the-art methods, particularly in high-variability conditions. Real-world robotic experiments further validate our method's robustness to sensor noise and physical uncertainties. Code is available at: https://github.com/wantingyao/hyper-goalnet.

Problem

• Develops goal-conditioned policy learning for robotic manipulation tasks
• Addresses performance maintenance across diverse objectives and environments
• Enhances robustness to sensor noise and physical uncertainties

Innovation

• Hypernetworks generate task-specific policy parameters from goals
• Separates goal interpretation from state processing via dual pathways
• Implements latent space constraints for dynamics and goal progression