Action Contextualization: Adaptive Task Planning and Action Tuning using Large Language Models

Large Language Models (LLMs) present a promising frontier in robotic task planning by leveraging extensive human knowledge. Nevertheless, the current literature often overlooks the critical aspects of adaptability and error correction within robotic systems. This work aims to overcome this limitation by enabling robots to modify their motion strategies and select the most suitable task plans based on the context. We introduce a novel framework termed action contextualization, aimed at tailoring robot actions to the precise requirements of specific tasks, thereby enhancing adaptability through applying LLM-derived contextual insights. Our proposed motion metrics guarantee the feasibility and efficiency of adjusted motions, which evaluate robot performance and eliminate planning redundancies. Moreover, our framework supports online feedback between the robot and the LLM, enabling immediate modifications to the task plans and corrections of errors. Our framework has achieved an overall success rate of 81.25% through extensive validation. Finally, integrated with dynamic system (DS)-based robot controllers, the robotic arm-hand system demonstrates its proficiency in autonomously executing LLM-generated motion plans for sequential table-clearing tasks, rectifying errors without human intervention, and completing tasks, showcasing robustness against external disturbances. Our proposed framework features the potential to be integrated with modular control approaches, significantly enhancing robots' adaptability and autonomy in sequential task execution.

Differentiable Robot Neural Distance Function for Adaptive Grasp Synthesis on a Unified Robotic Arm-Hand System

Grasping is a fundamental skill for robots to interact with their environment. While grasp execution requires coordinated movement of the hand and arm to achieve a collision-free and secure grip, many grasp synthesis studies address arm and hand motion planning independently, leading to potentially unreachable grasps in practical settings. The challenge of determining integrated arm-hand configurations arises from its computational complexity and high-dimensional nature. We address this challenge by presenting a novel differentiable robot neural distance function. Our approach excels in capturing intricate geometry across various joint configurations while preserving differentiability. This innovative representation proves instrumental in efficiently addressing downstream tasks with stringent contact constraints. Leveraging this, we introduce an adaptive grasp synthesis framework that exploits the full potential of the unified arm-hand system for diverse grasping tasks. Our neural joint space distance function achieves an 84.7% error reduction compared to baseline methods. We validated our approaches on a unified robotic arm-hand system that consists of a 7-DoF robot arm and a 16-DoF multi-fingered robotic hand. Results demonstrate that our approach empowers this high-DoF system to generate and execute various arm-hand grasp configurations that adapt to the size of the target objects while ensuring whole-body movements to be collision-free.