Jul 2026· 2026 23rd International Conference on Ubiquitous Robots (UR)· pp. 616-621· 0 citations· 18 references
Abstract
Deep reinforcement learning has enabled quadrupedal robots to traverse challenging terrains, yet energy efficiency remains a limiting factor for prolonged autonomous operation. Most existing frameworks rely on fixed or adaptively tuned proportional-derivative (PD) controllers that operate exclusively in the joint space. Such approaches typically lack an explicit mechanism for contact compliance, often applying excessive torque on benign terrains while providing insufficient absorption of reaction forces on irregular surfaces. To address these limitations, we propose HIP, a hybrid impedance and PD control framework that fuses joint-space PD control for trajectory tracking with task-space impedance control for contact compliance. The impedance term, mapped to joint torques via the Jacobian transpose, models compliant foot-tip behavior that absorbs impact energy during ground contact rather than resisting it through rigid control. To coordinate the two control modalities, we further introduce the attention for representation combiner (ARC) network. The ARC network employs a cross-attention mechanism between a gain actor and a joint actor, enabling control gains and desired joint positions to be generated in a coordinated manner. A state estimator augmented with a per-leg stumble estimator provides additional proprioceptive context to both actors for proactive gain adaptation. Simulation experiments across diverse terrains demonstrate that HIP achieves velocity tracking accuracy comparable to existing baselines while delivering improved energy efficiency. Torque decomposition analysis further confirms that the impedance component effectively reduces torque peaks during contact events.
Quadrupedal robots operating in unstructured environments require adaptive control systems that can handle diverse terrain conditions without prior surface characterization. This paper presents an integrated control architecture that combines Model Predictive Control (MPC) with adaptive impedance control and SINDy‐based model corrections for robust terrain‐adaptive locomotion. The system automatically detects surface properties through a four‐state contact detection mechanism and adapts control parameters in real‐time based on measured ground reaction forces. The impedance controller reduces foot slippage by 40% on challenging slopes, while an admittance control component improves force tracking accuracy by 40%–60% on compliant surfaces. SINDy corrections to angular velocity dynamics enhance yaw tracking performance by 80% compared to nominal rigid body models. Comprehensive validation in PyBullet demonstrates the system's effectiveness across diverse scenarios including slope navigation, soft surface adaptation, and complex trajectory tracking. The integrated approach establishes a robust framework for autonomous quadrupedal locomotion in unstructured environments without requiring prior terrain knowledge.
Peter James McConnellogue, Mien Van, Rhyss McMullan et al.· International Journal of Rob...· 0 citations
Multi-link aerial robots can actively deform their articulated structures during flight, giving them strong potential for aerial manipulation. However, they still face substantial challenges in contact-rich aerial manipulation tasks such as surface sliding, which requires both disturbance robustness and compliance to uncertain surface geometry. Force-control strategies such as impedance and admittance control are commonly employed to address these requirements. Although impedance control can provide disturbance-resistant interaction and admittance control can offer compliant adaptation, their opposite force--motion causalities prevent their simultaneous implementation when applied through the same actuation source, such as the rotor thrusts used by conventional aerial robots. To overcome this limitation, we propose a hybrid impedance--admittance control strategy for a multi-link aerial robot. The articulated morphology enables a functional separation of force and motion regulation across joint and rotor actuation sources. In this framework, admittance behavior is generated through joint angle regulation to enhance adaptive interaction, while impedance behavior is achieved by modulating rotor thrust to regulate the sliding motion. This structural coordination allows the robot to leverage the complementary strengths of both control paradigms. As a result, the multi-link aerial robot achieves resilient and adaptive surface sliding. Experimental results demonstrate robust and compliant sliding performance on unknown surfaces.
Zicheng Luo, Maolin Lei, Jinjie Li et al.· 0 citations
Equipping quadruped robots with manipulators significantly expands their operational workspace. However, for small-scale systems constrained by limited joint torques, achieving robust whole-body control on unstructured terrains remains a substantial challenge. Existing learning-based methods often face an inherent trade-off between locomotion stability and manipulation dexterity: traversing terrains introduces continuous base perturbations that constantly disturb state observations, significantly disrupting precise manipulation learning, whereas training exclusively on flat ground fails to yield robust locomotion skills for unstructured environments. To address these challenges, we propose QLIMB, a novel end-to-end whole-body control framework tailored for small-scale quadruped manipulators. We introduce a latent belief mixing mechanism that adaptively fuses mode-specific state representations to decouple state estimation for agile locomotion and stable manipulation within a unified policy, enabling seamless transitions between mobility and interaction modes. Furthermore, the policy exhibits emergent leg-arm coordination, ensuring smooth postural adaptations and intrinsic self-balancing during manipulation. Extensive real-world experiments demonstrate that QLIMB enables small-scale quadruped manipulators to achieve robust locomotion and stable manipulation on challenging terrains.
Quancheng Qian, Peng Zhai, Zonghao Zhang et al.· IEEE Robotics and Automation...· 0 citations
Quadruped robots have gained significant attention due to their superior mobility on uneven and unstructured terrains, offering potential applications in inspection, search and rescue, and field exploration. However, achieving robust locomotion on low-cost platforms remains challenging because of constraints in stability, adaptability, sensing quality, and onboard computation. In this work, we present an integrated motion-control framework that combines biologically inspired Central Pattern Generators (CPGs), a multi-agent reinforcement learning coordination layer, and low-cost hardware adaptation to enable reliable and efficient quadruped locomotion. The proposed framework uses CPGs as structured gait priors for rhythmic leg motion, models each leg as a coordinated agent with a shared-parameter residual policy, and incorporates actuator abstraction and safety-aware command projection. The low-cost merit specifically concerns online deployment: the four legs share a single 39,560-parameter actor (approximately 155 KiB in 32-bit precision), evaluated at 50 Hz from compact proprioceptive observations, while the centralized critic, simulation infrastructure, external motion capture, vision-based terrain perception, direct torque sensing, and online dynamics optimization are not required on the robot. We validate the approach in both simulation and on a physical low-cost quadruped robot across obstacles, ramps, stairs, and uneven terrain. Experimental results demonstrate that the integrated system improves locomotion stability, energy efficiency, and terrain adaptability compared with baseline controllers, highlighting the effectiveness of combining a structured gait prior, lightweight residual coordination, and hardware-aware deployment for practical quadruped locomotion.
Hardware failures require legged robots to rapidly reorganize coordination and gait timing to maintain stability and mobility. This is particularly challenging for larger quadrupeds, where increased mass and tighter actuation limits reduce the feasibility of aggressive, high-frequency compensation strategies often observed on smaller platforms. In this work, we propose a deep reinforcement learning approach for fault-tolerant locomotion under actuator power loss. The method employs an asymmetric actor-critic architecture in which the critic has access to privileged information during training, while the actor learns to reconstruct a corresponding latent representation from proprioceptive observations. We introduce a latent-alignment loss that encourages consistency between actor and critic representations. Additionally, we augment the action space with a learnable gait frequency parameter, enabling adaptive gait timing in response to terrain variations and actuator degradation without predefined faulty-leg strategies. The approach is validated in high-fidelity simulation on uneven terrain and real-world experiments on flat ground using a 68 kg quadruped robot.
Giovanbattista Gravina, Luca Rossini, Carlo Rizzardo et al.· 0 citations
The findings indicate that integrating reinforcement learning with Sigmoid parameter adaptation provides a systematic and effective solution for adaptive compliance regulation in mobile exoskeleton systems, enhancing adaptability, safety, and functional relevance for stroke patients undergoing lower limb rehabilitation training.