Unlocking the high-performance potential of Hydraulically-Driven Soft Robotic Arms (HDSRAs) requires computationally tractable dynamic models that are both physically faithful and rigorously validated, a combination that remains a critical challenge. This paper addresses this gap by presenting a systematic framework for the modeling, identification, and multi-faceted validation of such systems. Central to the framework is an enhanced coupled dynamic model incorporating often-neglected physical phenomena, including stiffness coupling, Rayleigh damping, and pressure-dependent hydraulics. The framework’s value is then established through a cohesive suite of four targeted experimental studies. An ablation study first quantitatively confirms the necessity of each model enhancement. A comparative analysis subsequently demonstrates the model’s superior accuracy against representative existing methods. A model-based feedforward control experiment then proves the model’s practical utility by significantly improving trajectory tracking performance. Finally, a generalization study on a more complex tri-chamber arm confirms the framework’s scalability. This work delivers not just a model, but a fully validated, high-fidelity “digital twin” that provides a solid foundation for designing high-performance controllers for a broad class of HDSRAs.

Jumping is a critical capability for quadruped robots, especially for navigating obstacles and gaps in complex environments. For successful jump, accurate trajectory tracking and robust feedback mechanism are essential, as cumulative deviations from the desired jumping trajectory can lead to instability or landing failure. Existing controllers often rely on fixed joint-level PD control or simplified inverse dynamics, which often fall short in tracking accuracy and robustness. In this paper, we propose a phase-aware iterative Linear Quadratic Regulator (iLQR) framework tailored for dynamic quadruped jumping tasks. By segmenting the jumping motion into distinct phases, we define phase-wise optimal control problem that respects the unique characteristics and requirements of each stage. Moreover, by leveraging a planar full-body dynamics of quadruped in each iLQR sub-problem, we derive a tracking controller consisting time-varying, full-state feedback gains, which shows better performance in tracking accuracy and disturbances rejection over traditional baseline controllers. Extensive simulation and hardware experiments on the Deeprobotics Lite3 quadruped validate the effectiveness and reliability of our proposed method in a number of dynamic jumping scenarios.

Soft crawling robots have attracted substantial attention for their environmental adaptability and compliant locomotion. However, the scalability of many soft in-pipe robots to large diameters remains limited because their locomotion typically relies on multi-point contact with the pipe inner wall. To overcome this limitation, we propose SMART-PIPEBOT, a woodpecker-tail-inspired adhesion-driven soft pipeline climbing robot that achieves single-sidewall attachment and locomotion in large-diameter pipes while markedly reducing dependence on distributed wall support. SMART-PIPEBOT integrates an inflatable silicone bladder, vacuum sucker brackets, silicone vacuum suckers, a constraining layer, and an elastic tail-support frame. Experiments quantified how vacuum-sucker inclination (10°–50°) affects adhesion at 0.05 MPa and evaluated single-sidewall climbing performance in large-diameter pipes (170, 200, and 230 mm). The maximum mean adhesion force was 26.778 N at a 30° inclination. SMART-PIPEBOT achieves maximum climbing speeds of 16.93 mm/s horizontally and 12.63 mm/s vertically under dry conditions and maintains continuous locomotion on wet walls with mean speeds of 4 mm/s (23.6% of the dry horizontal) and 2 mm/s (15.7% of the dry vertical). Payload experiments further demonstrate a pronounced improvement in effective load capacity when inspection equipment is mounted at the rear rather than the front, enabling a twofold increase in horizontal payload (100 g vs. 50 g) and a 1.5× increase in vertical payload (30 g vs. 20 g).

Recent advancements in integrating tactile sensing with vision-language models (VLMs) have demonstrated remarkable potential for robotic multimodal perception. However, existing tactile descriptions remain limited to superficial attributes like texture, neglecting critical contact states essential for robotic manipulation. To bridge this gap, we propose CLTP, an intuitive and effective language-tactile pre-training framework that aligns tactile 3D point clouds with natural language in various contact scenarios, thus enabling contact-state-aware tactile language understanding for contact-rich manipulation tasks. We first collect a novel dataset of 50k+ tactile 3D point cloud-language pairs, where descriptions explicitly capture multidimensional contact states (e.g., contact location, shape, and force) from the tactile sensor’s perspective. CLTP leverages a pre-aligned and frozen vision-language feature space to bridge holistic textual and tactile modalities. Experiments validate its superiority in three downstream tasks: zero-shot 3D classification, contact state classification, and tactile 3D large language model (LLM) interaction. To the best of our knowledge, this is the first study to align tactile and language representations from the contact state perspective for manipulation tasks, providing great potential for tactile-language-action model learning. Code and datasets are open-sourced at https://sites.google.com/view/cltp/ .