Goal State Generation for Robotic Manipulation Based on Linguistically Guided Hybrid Gaussian Diffusion

2024-12-25

AI Summary

In robotic hanging tasks, generating target end-effector poses that simultaneously satisfy semantic constraints, collision-free requirements, and physical feasibility remains challenging. This paper proposes a language-guided hybrid Gaussian diffusion model. Our method integrates a pretrained language encoder with a hybrid Gaussian prior to construct a semantics-controllable end-pose generation framework. We further introduce a gravity-coverage-coefficient-driven target-state refinement mechanism, which jointly optimizes geometric feasibility and static stability during the DDPM denoising process. Evaluated on a benchmark comprising 10 cup types × 5 shelf configurations × 10 hook placements, our approach achieves the highest hanging success rate, significantly reduces point-cloud overlap (indicating improved collision avoidance), and directly outputs collision-free, physically executable end-effector poses—eliminating the need for post-hoc motion planning or collision-avoidance optimization.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

In robotic manipulation tasks, achieving a designated target state for the manipulated object is often essential to facilitate motion planning for robotic arms. Specifically, in tasks such as hanging a mug, the mug must be positioned within a feasible region around the hook. Previous approaches have enabled the generation of multiple feasible target states for mugs; however, these target states are typically generated randomly, lacking control over the specific generation locations. This limitation makes such methods less effective in scenarios where constraints exist, such as hooks already occupied by other mugs or when specific operational objectives must be met. Moreover, due to the frequent physical interactions between the mug and the rack in real-world hanging scenarios, imprecisely generated target states from end-to-end models often result in overlapping point clouds. This overlap adversely impacts subsequent motion planning for the robotic arm. To address these challenges, we propose a Linguistically Guided Hybrid Gaussian Diffusion (LHGD) network for generating manipulation target states, combined with a gravity coverage coefficient-based method for target state refinement. To evaluate our approach under a language-specified distribution setting, we collected multiple feasible target states for 10 types of mugs across 5 different racks with 10 distinct hooks. Additionally, we prepared five unseen mug designs for validation purposes. Experimental results demonstrate that our method achieves the highest success rates across single-mode, multi-mode, and language-specified distribution manipulation tasks. Furthermore, it significantly reduces point cloud overlap, directly producing collision-free target states and eliminating the need for additional obstacle avoidance operations by the robotic arm.

Problem

• Robotics
• Precision Control
• Obstacle Avoidance

Innovation

• LHGD Network
• Gaussian Diffusion Methods
• Language-guided Robotics