--Service robots are increasingly deployed in diverse and dynamic environments, where both physical layouts and social contexts change over time and across locations. In these unstructured settings, conventional navigation systems that rely on fixed parameters often fail to generalize across scenarios, resulting in degraded performance and reduced social acceptance. Although recent approaches have leveraged reinforcement learning to enhance traditional planners, these methods often fail in real-world deployments due to poor generalization and limited simulation diversity, which hampers effective sim-to-real transfer . T o tackle these issues, we present LE-Nav, an interpretable and scene-aware navigation framework that leverages multi-modal large language model reasoning and conditional variational autoencoders to adaptively tune planner hyperpa-rameters. T o achieve zero-shot scene understanding, we utilize one-shot exemplars and chain-of-thought prompting strategies. Experiments show that LE-Nav can generate hyperparameters achieving human-level tuning across diverse planners and scenarios. Real-world navigation trials and a user study on a smart wheelchair platform demonstrate that it outperforms state-of-the-art methods on quantitative metrics such as success rate, efficiency, safety, and comfort, while receiving higher subjective scores for perceived safety and social acceptance. Note to Practitioners--Service robots often experience degraded performance of traditional local planners due to changing and dynamic environmental conditions during navigation. This work investigates automatic hyperparameter tuning for planners such as DW A and TEB, and our framework LE-Nav can be used to adjust hyperparameters of any optimization-based planner . Existing navigation frameworks are typically either end-to-end, lacking safety guarantees, or rely on reinforcement learning-based tuning with limited generalization. By designing two prompting strategies, we enable the MLLM to generate stable and accurate scene descriptions. We use a conditional variational autoencoder to learn human expert tuning strategies, enhanced with data augmentation and attention masking to address inevitable MLLM packet loss in real applications. The decoupling of the MLLM and action modules improves decision transparency, allowing clear insight into how scene analysis informs navigation behavior . Experiments demonstrate that our method adaptively generates hyperparameters comparable to human experts, while being robust to packet loss and compatible with various MLLMs. Future work includes enhancing real-time scene understanding with advanced MLLMs, expanding support to more planners with personalized tuning, and extending the framework to collaborative multi-robot systems.

For the cascaded planning and control modules implemented for robot navigation, the frequency gap between the planner and controller has received limited attention. In this study, we introduce a novel B-spline parameterized optimization-based planner (BSPOP) designed to address the frequency gap challenge with limited onboard computational power in robots. The proposed planner generates continuous-time control inputs for low-level controllers running at arbitrary frequencies to track. Furthermore, when considering the convex control action sets, BSPOP uses the convex hull property to automatically constrain the continuous-time control inputs within the convex set. Consequently, compared with the discrete-time optimization-based planners, BSPOP reduces the number of decision variables and inequality constraints, which improves computational efficiency as a byproduct. Simulation results demonstrate that our approach can achieve a comparable planning performance to the high-frequency baseline optimization-based planners while demanding less computational power. Both simulation and experiment results show that the proposed method performs better in planning compared with baseline planners in the same frequency.

Abstract-- Customized grippers have specifically designed fingers to increase the contact area with the workpieces and improve the grasp robustness. However, grasp planning for customized grippers is challenging due to the object variations, surface contacts and structural constraints of the grippers. In this paper, we propose a learning framework to plan robust grasps for customized grippers in real-time. The learning framework contains a low-level optimization-based planner to search for optimal grasps locally under object shape variations, and a high-level learning-based explorer to learn the grasp exploration based on previous grasp experience. The optimization-based planner uses an iterative surface fitting (ISF) to simultaneously search for optimal gripper transformation and finger displacement by minimizing the surface fitting error. The high-level learning-based explorer trains a regionbased convolutional neural network (R-CNN) to propose good optimization regions, which avoids ISF getting stuck in bad local optima and improves the collision avoidance performance. The proposed learning framework with RCNN-ISF is able to consider the structural constraints of the gripper, learn grasp exploration strategy from previous experience, and plan optimal grasps in clutter environment in real-time. The effectiveness of the algorithm is verified by experiments. Customized grippers have been broadly applied in industry to execute complex tasks such as assembly and packaging.