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Redundant manipulators are broadly used in safety tasks that require precise execution. However, some actual factors, such as assembly defects and mechanical wear, inevitably introduce uncertainties in their physical parameters. Due to the insufficient excitation, existing methods fail to achieve high-accuracy physical parameter identification, which seriously affects the accurate operation of multiple safety tasks. Considering that quadratic programming (QP) can integrate desired behaviors with constraints into a unified optimization framework and can be solved online in real time, this paper formulates the collision-free trajectory tracking of redundant manipulators with uncertain structure as a QP problem, which describes the trajectory tracking, obstacle avoidance and joint motion limits simultaneously. To tackle the above issues, we first develop an online physical parameters identification method called regularization and zeroing neural dynamics (RZND) by using the state information of the manipulator, which realizes highly accurate physical parameters identification and real-time Jacobian matrix updates. On the basis of this, a multi-task quadruple projection neural network (MT-QPNN) solver is proposed by applying projection operators to handle the joint constraints, and further the trajectory tracking and obstacle avoidance tasks of the redundant manipulator are well achieved. Relative experiments verify that the presented RZND method and MT-QPNN solver can accurately identify physical parameters and enable collision-free tracking with superior performance.
Humanoid robots have been increasingly deployed in real-world environments such as intelligent manufacturing, healthcare, and agriculture, from indoor spaces to outdoor terrains where friction conditions vary significantly. However, existing approaches such as domain randomization or employing estimation struggle to handle low-friction gait control and fail to address variable friction conditions, making it difficult to ensure control robustness. To address these challenges, we propose Walking on Ice (WoI), a friction-aware locomotion framework that combines proactive prediction with reactive adaptation for varying surface friction. We introduce a VLM-based friction predictor that enables the policy to take anticipatory actions before friction changing, thereby addressing variable-friction scenarios. Additionally, we design a friction estimator combined with a joint history encoder that perceives proprioceptive states to handle low-friction surfaces. Experimental results demonstrate that WoI improves success rate by 8.5% and reduces slip rate by 5.15% over the state-of-the-art method Denoising World Model Learning (DWL). Particularly, in extreme cases, WoI achieves 98.3% success rate with 51.0% higher survival rate, 3.20% lower slip rate, and 46.5% lower stuck rate than DWL, demonstrating stable and efficient gait adaptation. Our work provides a robust and efficient gait control framework for stable locomotion in real-world scenarios with dynamic friction conditions.
Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation, but their performance degrades significantly in long-horizon tasks due to cumulative error propagation. This limitation largely arises from static feature fusion mechanisms that rely on fixed weights to combine visual, language, and action representations, preventing the model from adapting to different phases of task execution. To address this limitation, we propose S²-VLA, a framework that introduces a State-Space Guided Adaptive Attention (SSGAA) mechanism. SSGAA maintains a belief state that tracks task progression and generates dynamic gating weights to adaptively fuse information from three complementary sources visual features for spatial perception, task intents for high-level task planning, and temporal action sequences for execution consistency. This adaptive fusion allows the model to shift its focus throughout task execution, aligning with the evolving requirements of different task stages. Despite its compact 2B parameter size, S²-VLA consistently outperforms larger 7B-scale models and achieves state-of-the-art performance on long-horizon manipulation benchmarks, including LIBERO and SimplerEnv. highlighting the importance of adaptive feature fusion for long-horizon robotic manipulation.
In human-aware vision-and-language navigation (HA-VLN), agents need to follow instructions while navigating safely among people in crowded environments. Existing methods often struggle to effectively transform forward-looking risk prediction into decision-making basis, resulting in intelligent agents being prone to collisions in crowded scenarios and taking overly cautious actions, thereby reducing navigation efficiency. Moreover, it is difficult for high-level decisions based on social norms to be stably implemented in dynamic crowd scenarios, which can easily lead to behavioral jitter and planning deviation. In this work, we propose the AHead-VLN, a navigation framework that incorporates forward-looking risk information in the reasoning process. Specifically, we designed the path risk association (PRA) module, which compiles future risks into structured risk events aligned with each candidate path, making risk information decision-ready for the large language model (LLM) reasoning. To balance executability and decision stability, we have further designed the hierarchical LLM planner (HLP) with two specialized components. The ``Brain'' LLM makes route choices based on instructions and risks, while the ``Cerebellum'' LLM prepares executable action scripts for candidate routes and retrieves the appropriate script for execution. Experiments on the HA-VLN benchmark show that AHead-VLN improves success while enhancing safety compared to strong baselines.
Mobile Manipulation (MM) involves long-horizon decision-making over multi-stage compositions of heterogeneous skills, such as navigation and picking up objects. Despite recent progress, existing MM methods still face two key limitations: (i) low sample efficiency, due to ineffective use of redundant data generated during long-term MM interactions; and (ii) poor spatial generalization, as policies trained on specific tasks struggle to transfer to new spatial layouts without additional training. In this paper, we address these challenges through Adaptive Experience Selection (AES) and model-based dynamic imagination. In particular, AES makes MM agents pay more attention to critical experience fragments in long trajectories that affect task success, improving skill chain learning and mitigating skill forgetting. Based on AES, a Recurrent State-Space Model (RSSM) is introduced for Model-Predictive Forward Planning (MPFP) by capturing the coupled dynamics between the mobile base and the manipulator and imagining the dynamics of future manipulations. RSSM-based MPFP can reinforce MM skill learning on the current task while enabling effective generalization to new spatial layouts. Comparative studies across different experimental configurations demonstrate that our method significantly outperforms existing MM policies. Real-world experiments further validate the feasibility and practicality of our method. The source code is available at https://csu-hero-lab.github.io/SG-MM Web.