Loïc Niederhauser | Searcharxiv

arXiv2025

Implicit Articulated Robot Morphology Modeling with Configuration Space Neural Signed Distance Functions

In this paper, we introduce a novel approach to implicitly encode precise robot morphology using forward kinematics based on a configuration space signed distance function. Our proposed Robot Neural Distance Function (RNDF) optimizes the balance between computational efficiency and accuracy for signed distance queries conditioned on the robot's configuration for each link. Compared to the baseline method, the proposed approach achieves an 81.1% reduction in distance error while utilizing only 47.6% of model parameters. Its parallelizable and differentiable nature provides direct access to joint-space derivatives, enabling a seamless connection between robot planning in Cartesian task space and configuration space. These features make RNDF an ideal surrogate model for general robot optimization and learning in 3D spatial planning tasks. Specifically, we apply RNDF to robotic arm-hand modeling and demonstrate its potential as a core platform for whole-arm, collision-free grasp planning in cluttered environments. The code and model are available at https://github.com/robotic-manipulation/RNDF.

cs.RO↗

arXiv2024

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 robots' adaptability and error correction. This work aims to overcome this limitation by enabling robots to modify their motions and select the most suitable task plans based on the context. We introduce a novel framework to achieve action contextualization, aimed at tailoring robot actions to the context of specific tasks, thereby enhancing adaptability through applying LLM-derived contextual insights. Our framework integrates motion metrics that evaluate robot performances for each motion to resolve redundancy in planning. Moreover, it supports online feedback between the robot and the LLM, enabling immediate modifications to the task plans and corrections of errors. An overall success rate of 81.25% has been achieved through extensive experimental validation. Finally, when integrated with dynamical 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 showcasing robustness against external disturbances. Our proposed framework also features the potential to be integrated with modular control approaches, significantly enhancing robots' adaptability and autonomy in performing sequential tasks in the real world.

cs.RO↗